system

Abstract

The system according to this embodiment aims to consistently provide personalized healthy meal suggestions and deliveries based on the user's health condition and dietary preferences. [Solution] The system according to the embodiment comprises a reception unit, a decision unit, a selection unit, and an order unit. The reception unit receives the user's health status and food preferences. The decision unit determines the menu based on the information received by the reception unit. The selection unit selects a restaurant to deliver to based on the menu determined by the decision unit. The order unit orders the menu determined by the decision unit from the restaurant selected by the selection unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that it is difficult to consistently propose and deliver individual healthy foods based on the health status and dietary preferences of users.

[0005] The system according to the embodiment aims to consistently propose and deliver individual healthy foods based on the health status and dietary preferences of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a decision unit, a selection unit, and an order unit. The reception unit receives the user's health status and dietary preferences. The decision unit determines the menu based on the information received by the reception unit. The selection unit selects a restaurant to deliver to based on the menu determined by the decision unit. The order unit orders the menu determined by the decision unit from the restaurant selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can consistently provide personalized healthy meal suggestions and deliveries based on the user's health condition and dietary preferences. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[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 healthy meal suggestion system according to an embodiment of the present invention is a system that takes the user's health condition (e.g., hypertension or dyslipidemia) and dietary preferences as input and consistently provides personalized healthy meal suggestions and deliveries based on that information. The healthy meal suggestion system aims to alleviate the burden on users who want to diet for health reasons but find it difficult to plan daily menus. First, the user inputs their health condition and dietary preferences into the system. For example, if the user has hypertension and dyslipidemia, they input that information. They also input their favorite and disliked ingredients. This information is entered into the system's reception section. Next, based on the information received by the reception section, the generating AI determines the menu. The generating AI considers the user's health condition and dietary preferences and proposes a nutritionally balanced menu. For example, it proposes a low-salt menu for a user with hypertension and a low-fat menu for a user with dyslipidemia. After that, based on the determined menu, the generating AI selects a restaurant for delivery. The generating AI considers the user's location and the delivery area to select the most suitable restaurant. For example, it selects a restaurant that provides healthy meals in the user's area. Finally, the AI ​​generates an order for the selected menu from the chosen restaurant. The order is placed automatically, and the user does not need to do anything special, and healthy meals are delivered. For example, if a user orders a "low-sodium salad," that menu item will be automatically ordered from the selected restaurant and delivered. This system reduces the burden of daily meal preparation for users and allows them to easily enjoy nutritionally balanced, healthy meals. It can also accommodate special meal requests, making it possible to provide meals tailored to the user's health condition. As a result, the healthy meal suggestion system can consistently suggest and deliver individualized healthy meals based on the user's health condition and dietary preferences.

[0029] The healthy meal suggestion system according to this embodiment comprises a reception unit, a decision unit, a selection unit, and an order unit. The reception unit receives the user's health status and dietary preferences. The user's health status includes, but is not limited to, blood pressure, blood glucose levels, and weight. Dietary preferences include, but are not limited to, favorite foods, disliked foods, and allergy information. The reception unit stores the user's health status and dietary preference information in a database, for example. The reception unit can also analyze the user's input information in real time and generate data for appropriate menu suggestions. For example, the reception unit analyzes the user's health status and dietary preference trends based on the information entered by the user. The decision unit uses a generation AI to determine a menu based on the information received by the reception unit. The generation AI, for example, considers the user's health status and dietary preferences and suggests a nutritionally balanced menu. For example, the generation AI suggests a low-salt menu to a user with high blood pressure. The generation AI can also suggest a low-fat menu to a user with dyslipidemia. Furthermore, the generation AI can suggest a menu that meets special dietary requirements. For example, the generation AI suggests menus that cater to special dietary requests such as vegetarian, gluten-free, and low-carb options. The selection unit selects a delivery restaurant based on the menu determined by the decision unit. The selection unit selects the most suitable restaurant by considering factors such as the user's location and delivery area. For example, the selection unit selects a restaurant that offers healthy meals in the user's area. The selection unit can also select a restaurant by considering factors such as delivery ratings and delivery time. The ordering unit orders the menu determined by the decision unit from the restaurant selected by the selection unit. The ordering unit automatically places orders with the selected restaurants. For example, the ordering unit automates the ordering process using API integration. The ordering unit is also designed so that the user can complete the order without any special operation. As a result, the healthy meal suggestion system according to this embodiment can consistently suggest and deliver individualized healthy meals based on the user's health condition and dietary preferences.

[0030] The reception desk receives information about the user's health status and dietary preferences. A user's health status includes, but is not limited to, blood pressure, blood sugar levels, and weight. Specifically, users can input this health data through a dedicated application or website. The entered data is stored in a secure database and encrypted for privacy protection. Dietary preferences include, but are not limited to, favorite and disliked foods and allergy information. Users can input detailed information about their food preferences and allergies, allowing the system to suggest menus tailored to their individual needs. The reception desk stores the health status and dietary preference information entered by the user in a database. Furthermore, the reception desk can analyze the user's input in real time and generate data for appropriate menu suggestions. For example, the reception desk analyzes trends in health status and dietary preferences based on the information entered by the user. This allows for quick identification of changes in the user's health status and dietary preferences, enabling optimal menu suggestions. Additionally, the reception desk accumulates the user's past data to support long-term health management. For example, by analyzing past blood pressure and blood sugar data, it's possible to analyze trends in a user's health status and predict future risks. This provides the reception desk with a foundation for making personalized healthy meal suggestions based on the user's health status and dietary preferences.

[0031] The decision-making unit uses a generation AI to determine the menu based on the information received by the reception unit. For example, the generation AI considers the user's health condition and dietary preferences to suggest a nutritionally balanced menu. Specifically, the generation AI analyzes the user's health data such as blood pressure, blood sugar levels, and weight, and calculates the optimal balance of nutrients based on this. For example, it suggests a low-sodium menu for users with high blood pressure. The generation AI can also suggest a low-fat menu for users with dyslipidemia. Furthermore, the generation AI can suggest menus that accommodate special dietary requests. For example, it can suggest menus that accommodate special dietary requests such as vegetarian, gluten-free, and low-carbohydrate diets. The generation AI utilizes a vast recipe database to generate menus that are optimal for the user's individual needs. For example, the generation AI considers the user's favorite and disliked ingredients to suggest menus that enhance meal satisfaction. The generation AI also considers allergy information to suggest safe menus that will not cause allergic reactions. Furthermore, the generation AI can analyze the user's past eating history and make suggestions to provide variety in their meals. This enables the decision-making unit to perform sophisticated menu determination to provide personalized healthy meal suggestions based on the user's health condition and dietary preferences.

[0032] The selection unit selects restaurants to deliver based on the menu determined by the decision unit. The selection unit considers factors such as the user's location and delivery area to select the most suitable restaurant. Specifically, it obtains the user's current location information and lists several restaurants offering healthy meals in that area. For example, it selects restaurants offering healthy meals in the user's residential area. The selection unit can also select restaurants considering factors such as delivery ratings and delivery times. For example, it prioritizes highly reliable restaurants based on past delivery ratings and delivery time data. Furthermore, it considers the restaurant's menu and price range to select a restaurant that matches the user's budget and preferences. This allows the selection unit to select the most suitable delivery restaurant for the user, ensuring quick and reliable delivery of healthy meals. Additionally, the selection unit analyzes the user's past order history to facilitate smoother selection for repeat orders. For example, it prioritizes displaying restaurants and menus previously ordered by the user, making reordering easier. Furthermore, the selection unit can collect user feedback and continuously improve the accuracy of its selection algorithm. This allows the selection unit to select the most suitable delivery store for each user and efficiently provide healthy meals.

[0033] The ordering unit places orders with the selected restaurants, based on the menus determined by the decision-making unit. For example, the ordering unit automatically places orders with the selected restaurants. Specifically, the ordering unit automates the ordering process using API integration. For instance, it integrates with the online ordering system of the selected restaurants and automatically sends the user's order details. Furthermore, the ordering unit is designed to complete orders without requiring any special user intervention. For example, it allows users to easily place subsequent orders based on information they have entered once. In addition, the ordering unit can track the order progress in real time and notify the user. For example, it can send notifications to the user at each stage, such as when the order is accepted, when cooking begins, and when delivery begins, allowing them to wait with peace of mind. The ordering unit also automates the payment process, enabling users to complete payments smoothly. For example, it supports multiple payment methods, such as credit cards and electronic money, to enhance user convenience. This allows the ordering unit to efficiently manage the ordering process to provide users with healthy meals quickly and reliably, thereby improving user satisfaction.

[0034] The decision unit can determine a menu based on the user's health checkup information. For example, the decision unit may suggest a nutritionally balanced menu based on the user's health checkup information. For example, the decision unit may determine a menu considering the user's diagnosis results and the doctor's comments. The decision unit can also analyze the user's health checkup information in real time and generate data for appropriate menu suggestions. For example, the decision unit may suggest a menu fortified with specific nutrients based on the user's health checkup information. This allows for the determination of a more appropriate menu based on the user's health checkup information. Health checkup information includes, but is not limited to, diagnosis results and doctor's comments. Some or all of the above processing in the decision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision unit can input the user's health checkup information into a generation AI, and the generation AI can determine a menu.

[0035] The decision unit can determine a menu that accommodates special dietary requests. For example, the decision unit can suggest menus that address special dietary requests such as vegetarian, gluten-free, or low-carbohydrate diets. For instance, the decision unit considers the user's special dietary requests and determines an appropriate menu. The decision unit can also suggest menus using alternative ingredients to accommodate special dietary requests. For example, the decision unit might suggest a menu using gluten-free ingredients to a gluten-free user. This allows the decision unit to determine a menu that addresses special dietary requests. Special dietary requests include, but are not limited to, vegetarian, gluten-free, and low-carbohydrate diets. Some or all of the above processing in the decision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision unit can input the user's special dietary requests into a generative AI, which can then determine a menu.

[0036] The selection unit can select restaurants based on the user's location and delivery range. For example, the selection unit can select the optimal restaurant based on the user's location. For example, the selection unit can select a restaurant that offers healthy food in the area where the user lives. The selection unit can also select restaurants considering the delivery range. For example, the selection unit can select restaurants considering delivery ratings and delivery times. This allows the selection of the optimal restaurant to be made considering the user's location and delivery range. Location includes, but is not limited to, GPS data and address information. Delivery range includes, but is not limited to, distance and delivery time. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's location data into AI, and the AI ​​can select the optimal restaurant.

[0037] The ordering unit can automatically place orders with selected stores. For example, the ordering unit can automatically place orders with selected stores. For example, the ordering unit can automate the ordering process using API integration. The ordering unit is also designed so that orders can be completed without any special action by the user. For example, the ordering unit can automatically send orders to selected stores and arrange delivery. This allows for automatic ordering with selected stores. Methods for automatic ordering include, but are not limited to, API integration and automation of the ordering process. Some or all of the above processes in the ordering unit may or may not be performed using AI. For example, the ordering unit can input information about selected stores into AI, which can then automatically place orders.

[0038] The reception desk can analyze the user's past health check data and provide the optimal input format. For example, the reception desk can automatically input the necessary items based on the user's past health check data. The reception desk can also suggest prioritizing the input of specific items based on the user's past health check data. Furthermore, the reception desk can analyze the user's past health check data and customize the input format. This allows the reception desk to provide the optimal input format based on the user's past health check data. Past health check data includes, but is not limited to, diagnostic history and test results. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health check data into an AI, which can then provide the optimal input format.

[0039] The reception unit can filter inputs regarding health status and dietary preferences, taking into account the user's lifestyle and exercise level. For example, the reception unit can filter input items based on the user's lifestyle data. The reception unit can also adjust input items considering the user's exercise level data. Furthermore, the reception unit can comprehensively analyze the user's lifestyle and exercise level and suggest the most suitable input items. This allows the system to filter input items considering the user's lifestyle and exercise level. Lifestyle includes, but is not limited to, eating patterns, sleep duration, and exercise habits. Exercise level includes, but is not limited to, steps taken, calories burned, and exercise time. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into AI, which can then filter the input items.

[0040] The reception desk can prioritize the acquisition of highly relevant data when users input their health status and dietary preferences, taking into account their geographical location. For example, the reception desk can prioritize acquiring region-specific health information based on the user's current location. It can also prioritize acquiring relevant food information, taking into account the user's geographical location. Furthermore, it can prioritize acquiring regional health trends based on the user's geographical location. This allows the reception desk to prioritize the acquisition of highly relevant data, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant data includes, but is not limited to, information on nearby stores and information on local food ingredients. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize the acquisition of highly relevant data.

[0041] The reception desk can analyze the user's social media activity and obtain relevant data when the user inputs their health status and dietary preferences. For example, the reception desk can analyze the user's social media posts and obtain data related to their health status and dietary preferences. The reception desk can also analyze posts from the user's social media followers and friends and obtain relevant data. Furthermore, the reception desk can obtain data related to their health status and dietary preferences based on the user's social media activity history. This allows the reception desk to analyze the user's social media activity and obtain relevant data. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then obtain relevant data.

[0042] The decision-making unit can analyze the user's past eating history and suggest menus when determining a menu. For example, the decision-making unit can suggest a nutritionally balanced menu based on the user's past eating history. It can also suggest a menu that includes the user's preferred ingredients based on their past eating history. Furthermore, the decision-making unit can analyze the user's past eating history and suggest a menu that is appropriate for their health condition. In this way, it can analyze the user's past eating history and suggest the optimal menu. Past eating history includes, but is not limited to, meal records and order history. Some or all of the above processing in the decision-making unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision-making unit can input the user's past eating history into a generation AI, and the generation AI can suggest a menu.

[0043] The decision-making unit can customize the menu when determining the menu, taking into account the user's allergy information. For example, the decision-making unit can suggest a menu that does not contain allergens based on the user's allergy information. The decision-making unit can also suggest a menu that uses alternative ingredients, taking into account the user's allergy information. Furthermore, the decision-making unit can customize a menu that avoids allergens based on the user's allergy information. In this way, the menu can be customized taking into account the user's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergy. Some or all of the above processing in the decision-making unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision-making unit can input the user's allergy information into a generation AI, and the generation AI can customize the menu.

[0044] The decision unit can determine the priority of menu items based on the user's meal timing when selecting a menu. For example, if the user requests breakfast, the decision unit will prioritize suggesting menu items suitable for breakfast. Similarly, if the user requests lunch, the decision unit can prioritize suggesting menu items suitable for lunch. Furthermore, if the user requests dinner, the decision unit can prioritize suggesting menu items suitable for dinner. This allows the decision unit to determine menu priorities based on the user's meal timing. Meal timing includes, but is not limited to, breakfast, lunch, and dinner times. Some or all of the above processing in the decision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision unit can input the user's meal timing data into a generative AI, which can then determine the menu priorities.

[0045] The decision-making unit can adjust the order of menu items based on the relevance of the user's meals when determining the menu. For example, the decision-making unit can suggest the next menu item based on the relevance of menu items the user has previously selected. The decision-making unit can also analyze the user's eating patterns and suggest highly relevant menu items. Furthermore, the decision-making unit can consider the user's eating preferences and suggest highly relevant menu items in order. This allows the order of menu items to be adjusted based on the relevance of the user's meals. Relevance of meals includes, but is not limited to, combinations of ingredients and nutritional balance. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit can input the user's meal relevance data into a generative AI, which can then adjust the order of the menu items.

[0046] The selection unit can select a restaurant by analyzing the user's past order history. For example, the selection unit can select a restaurant based on the user's past order history. The selection unit can also select a high-quality restaurant from the user's past order history. Furthermore, the selection unit can analyze the user's past order history and select the restaurant with the highest satisfaction rate. In this way, the optimal restaurant can be selected by analyzing the user's past order history. Past order history includes, but is not limited to, order date and time, order details, and ratings. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past order history data into AI, and the AI ​​can select the optimal restaurant.

[0047] The selection unit can customize restaurants based on the user's food preferences when selecting a restaurant. For example, the selection unit can select restaurants that offer ingredients preferred by the user. It can also select restaurants that offer specific dishes based on the user's food preferences. Furthermore, the selection unit can select restaurants that offer customizable menus, taking into account the user's food preferences. This allows for the customization of restaurants based on the user's food preferences. Food preferences include, but are not limited to, favorite dishes, disliked dishes, and allergy information. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's food preference data into an AI, which can then customize the restaurants.

[0048] The selection unit can select restaurants considering the geographical distribution of users. For example, the selection unit can select the nearest restaurant based on the user's current location. It can also select restaurants within a delivery range, considering the user's geographical distribution. Furthermore, the selection unit can select restaurants that offer regionally specific cuisine based on the user's geographical distribution. This allows for the selection of the optimal restaurant, taking into account the user's geographical distribution. Geographical distribution includes, but is not limited to, user address information and GPS data. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user geographical distribution data into AI, which can then select the optimal restaurant.

[0049] The selection unit can improve the accuracy of its selections by referring to the user's relevant literature when selecting restaurants. For example, the selection unit can select restaurants that are considered healthy based on the user's relevant literature. The selection unit can also select restaurants that use specific ingredients by referring to the user's relevant literature. Furthermore, the selection unit can select restaurants that offer nutritionally balanced menus based on the user's relevant literature. This allows the selection unit to improve the accuracy of its selections by referring to the user's relevant literature. Relevant literature includes, but is not limited to, academic papers, technical reports, and patent documents. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's relevant literature data into AI, which can then improve the accuracy of its selections.

[0050] The ordering department can analyze the user's past order history to select the optimal ordering method at the time of ordering. For example, the ordering department can suggest a preferred ordering method based on the user's past order history. The ordering department can also suggest a faster ordering method based on the user's past order history. Furthermore, the ordering department can analyze the user's past order history and suggest the ordering method that will provide the highest satisfaction. This allows the ordering department to analyze the user's past order history and select the optimal ordering method. Past order history includes, but is not limited to, order date and time, order details, and ratings. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's past order history data into AI, which can then select the optimal ordering method.

[0051] The ordering system can customize the ordering process based on the user's current circumstances at the time of ordering. For example, if the user is busy, the ordering system can provide a quick ordering method. Alternatively, if the user is relaxed, the ordering system can provide a detailed ordering method. Furthermore, the ordering system can suggest the most suitable ordering method considering the user's circumstances. This allows the ordering process to be customized based on the user's current circumstances. Current circumstances include, but are not limited to, work situation, family situation, and health status. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input the user's circumstances data into an AI, which can then suggest the most suitable ordering method.

[0052] The ordering department can select the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering department can suggest the fastest delivery method based on the user's current location. The ordering department can also set the optimal delivery range, taking into account the user's geographical location. Furthermore, the ordering department can suggest region-specific ordering methods based on the user's geographical location. This allows the ordering department to select the optimal ordering method, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's geographical location information into AI, which can then select the optimal ordering method.

[0053] The ordering department can analyze the user's social media activity and suggest ordering methods when an order is placed. For example, the ordering department can analyze the user's social media posts and suggest preferred ordering methods. It can also analyze posts from the user's social media followers and friends and suggest relevant ordering methods. Furthermore, the ordering department can suggest the optimal ordering method based on the user's social media activity history. This allows the system to analyze the user's social media activity and suggest the most suitable ordering method. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's social media data into an AI, which can then suggest the optimal ordering method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The healthy meal suggestion system can also acquire user exercise data and incorporate it into the suggested menus. For example, it can acquire data such as the type and frequency of exercise the user performs daily and the calories burned, and then suggest menus that require energy replenishment based on this data. It can also suggest recovery menus using ingredients rich in protein after exercise. Furthermore, based on exercise data, it can provide nutritionally balanced menus tailored to the user's exercise goals. This enables the suggestion of healthy meals that match the user's exercise habits, leading to more effective health management.

[0056] A healthy meal suggestion system can analyze a user's past eating history and reflect that in the suggested menus. For example, it can suggest similar menus based on dishes and ingredients the user has enjoyed eating in the past. It can also analyze nutritional imbalances from past eating history and suggest menus that supplement necessary nutrients. Furthermore, it can understand the user's eating patterns based on past eating history and suggest meals at appropriate times. This enables more personalized menu suggestions that utilize the user's past eating history.

[0057] The healthy eating suggestion system can propose menus using local ingredients, taking into account the user's geographical location. For example, it can suggest menus using fresh ingredients harvested in the user's area. It can also suggest menus incorporating traditional local dishes and seasonal ingredients. Furthermore, it can suggest menus using ingredients directly sourced from local farmers and producers. This enables the provision of locally-rooted healthy eating suggestions that leverage the user's geographical location.

[0058] A healthy meal suggestion system can acquire users' lifestyle data and incorporate it into the suggested menus. For example, it can suggest menus using ingredients with relaxing effects based on the user's sleep patterns and stress levels. It can also suggest easy-to-prepare menus considering the user's work schedule and family situation. Furthermore, it can provide menus tailored to specific health goals based on the user's lifestyle data. This enables the suggestion of healthy meals that are tailored to the user's lifestyle, leading to more effective health management.

[0059] A healthy meal suggestion system can analyze users' social media activity and reflect it in the menu suggestions. For example, it can suggest menus based on photos and comments of meals shared by users on social media. It can also analyze posts from users' followers and friends to suggest menus that match current trends. Furthermore, it can suggest menus tailored to specific events or seasons based on the user's social media activity history. This enables more personalized menu suggestions that leverage users' social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives the user's health status and dietary preferences. The user's health status includes, but is not limited to, blood pressure, blood sugar levels, and weight. Dietary preferences include, but are not limited to, favorite foods, disliked foods, and allergy information. The reception desk stores the health status and dietary preference information entered by the user in a database. The reception desk can also analyze the information entered by the user in real time and generate data for appropriate menu suggestions. For example, the reception desk analyzes trends in health status and dietary preferences based on the information entered by the user. Step 2: The decision unit uses a generation AI to determine the menu based on the information received by the reception unit. The generation AI considers the user's health condition and dietary preferences, for example, and proposes a nutritionally balanced menu. For example, the generation AI will suggest a low-sodium menu for a user with high blood pressure. It can also suggest a low-fat menu for a user with dyslipidemia. Furthermore, the generation AI can also propose menus that accommodate special dietary requests. For example, it can suggest menus that accommodate special dietary requests such as vegetarian, gluten-free, or low-carbohydrate options. Step 3: The selection unit selects restaurants to deliver based on the menu determined by the decision unit. The selection unit selects the most suitable restaurants by considering, for example, the user's location and the delivery area. For example, the selection unit selects restaurants that offer healthy food in the user's area. The selection unit can also select restaurants by considering factors such as delivery ratings and delivery time. Step 4: The ordering unit places an order for the menu determined by the decision unit from the restaurant selected by the selection unit. The ordering unit can, for example, automatically place an order with the selected restaurant. For example, the ordering unit can automate the ordering process using API integration. The ordering unit is also designed so that the user can complete the order without any special actions.

[0062] (Example of form 2) The healthy meal suggestion system according to an embodiment of the present invention is a system that takes the user's health condition (e.g., hypertension or dyslipidemia) and dietary preferences as input and consistently provides personalized healthy meal suggestions and deliveries based on that information. The healthy meal suggestion system aims to alleviate the burden on users who want to diet for health reasons but find it difficult to plan daily menus. First, the user inputs their health condition and dietary preferences into the system. For example, if the user has hypertension and dyslipidemia, they input that information. They also input their favorite and disliked ingredients. This information is entered into the system's reception section. Next, based on the information received by the reception section, the generating AI determines the menu. The generating AI considers the user's health condition and dietary preferences and proposes a nutritionally balanced menu. For example, it proposes a low-salt menu for a user with hypertension and a low-fat menu for a user with dyslipidemia. After that, based on the determined menu, the generating AI selects a restaurant for delivery. The generating AI considers the user's location and the delivery area to select the most suitable restaurant. For example, it selects a restaurant that provides healthy meals in the user's area. Finally, the AI ​​generates an order for the selected menu from the chosen restaurant. The order is placed automatically, and the user does not need to do anything special, and healthy meals are delivered. For example, if a user orders a "low-sodium salad," that menu item will be automatically ordered from the selected restaurant and delivered. This system reduces the burden of daily meal preparation for users and allows them to easily enjoy nutritionally balanced, healthy meals. It can also accommodate special meal requests, making it possible to provide meals tailored to the user's health condition. As a result, the healthy meal suggestion system can consistently suggest and deliver individualized healthy meals based on the user's health condition and dietary preferences.

[0063] The healthy meal suggestion system according to this embodiment comprises a reception unit, a decision unit, a selection unit, and an order unit. The reception unit receives the user's health status and dietary preferences. The user's health status includes, but is not limited to, blood pressure, blood glucose levels, and weight. Dietary preferences include, but are not limited to, favorite foods, disliked foods, and allergy information. The reception unit stores the user's health status and dietary preference information in a database, for example. The reception unit can also analyze the user's input information in real time and generate data for appropriate menu suggestions. For example, the reception unit analyzes the user's health status and dietary preference trends based on the information entered by the user. The decision unit uses a generation AI to determine a menu based on the information received by the reception unit. The generation AI, for example, considers the user's health status and dietary preferences and suggests a nutritionally balanced menu. For example, the generation AI suggests a low-salt menu to a user with high blood pressure. The generation AI can also suggest a low-fat menu to a user with dyslipidemia. Furthermore, the generation AI can suggest a menu that meets special dietary requirements. For example, the generation AI suggests menus that cater to special dietary requests such as vegetarian, gluten-free, and low-carb options. The selection unit selects a delivery restaurant based on the menu determined by the decision unit. The selection unit selects the most suitable restaurant by considering factors such as the user's location and delivery area. For example, the selection unit selects a restaurant that offers healthy meals in the user's area. The selection unit can also select a restaurant by considering factors such as delivery ratings and delivery time. The ordering unit orders the menu determined by the decision unit from the restaurant selected by the selection unit. The ordering unit automatically places orders with the selected restaurants. For example, the ordering unit automates the ordering process using API integration. The ordering unit is also designed so that the user can complete the order without any special operation. As a result, the healthy meal suggestion system according to this embodiment can consistently suggest and deliver individualized healthy meals based on the user's health condition and dietary preferences.

[0064] The reception desk receives information about the user's health status and dietary preferences. A user's health status includes, but is not limited to, blood pressure, blood sugar levels, and weight. Specifically, users can input this health data through a dedicated application or website. The entered data is stored in a secure database and encrypted for privacy protection. Dietary preferences include, but are not limited to, favorite and disliked foods and allergy information. Users can input detailed information about their food preferences and allergies, allowing the system to suggest menus tailored to their individual needs. The reception desk stores the health status and dietary preference information entered by the user in a database. Furthermore, the reception desk can analyze the user's input in real time and generate data for appropriate menu suggestions. For example, the reception desk analyzes trends in health status and dietary preferences based on the information entered by the user. This allows for quick identification of changes in the user's health status and dietary preferences, enabling optimal menu suggestions. Additionally, the reception desk accumulates the user's past data to support long-term health management. For example, by analyzing past blood pressure and blood sugar data, it's possible to analyze trends in a user's health status and predict future risks. This provides the reception desk with a foundation for making personalized healthy meal suggestions based on the user's health status and dietary preferences.

[0065] The decision-making unit uses a generation AI to determine the menu based on the information received by the reception unit. For example, the generation AI considers the user's health condition and dietary preferences to suggest a nutritionally balanced menu. Specifically, the generation AI analyzes the user's health data such as blood pressure, blood sugar levels, and weight, and calculates the optimal balance of nutrients based on this. For example, it suggests a low-sodium menu for users with high blood pressure. The generation AI can also suggest a low-fat menu for users with dyslipidemia. Furthermore, the generation AI can suggest menus that accommodate special dietary requests. For example, it can suggest menus that accommodate special dietary requests such as vegetarian, gluten-free, and low-carbohydrate diets. The generation AI utilizes a vast recipe database to generate menus that are optimal for the user's individual needs. For example, the generation AI considers the user's favorite and disliked ingredients to suggest menus that enhance meal satisfaction. The generation AI also considers allergy information to suggest safe menus that will not cause allergic reactions. Furthermore, the generation AI can analyze the user's past eating history and make suggestions to provide variety in their meals. This enables the decision-making unit to perform sophisticated menu determination to provide personalized healthy meal suggestions based on the user's health condition and dietary preferences.

[0066] The selection unit selects restaurants to deliver based on the menu determined by the decision unit. The selection unit considers factors such as the user's location and delivery area to select the most suitable restaurant. Specifically, it obtains the user's current location information and lists several restaurants offering healthy meals in that area. For example, it selects restaurants offering healthy meals in the user's residential area. The selection unit can also select restaurants considering factors such as delivery ratings and delivery times. For example, it prioritizes highly reliable restaurants based on past delivery ratings and delivery time data. Furthermore, it considers the restaurant's menu and price range to select a restaurant that matches the user's budget and preferences. This allows the selection unit to select the most suitable delivery restaurant for the user, ensuring quick and reliable delivery of healthy meals. Additionally, the selection unit analyzes the user's past order history to facilitate smoother selection for repeat orders. For example, it prioritizes displaying restaurants and menus previously ordered by the user, making reordering easier. Furthermore, the selection unit can collect user feedback and continuously improve the accuracy of its selection algorithm. This allows the selection unit to select the most suitable delivery store for each user and efficiently provide healthy meals.

[0067] The ordering unit places orders with the selected restaurants, based on the menus determined by the decision-making unit. For example, the ordering unit automatically places orders with the selected restaurants. Specifically, the ordering unit automates the ordering process using API integration. For instance, it integrates with the online ordering system of the selected restaurants and automatically sends the user's order details. Furthermore, the ordering unit is designed to complete orders without requiring any special user intervention. For example, it allows users to easily place subsequent orders based on information they have entered once. In addition, the ordering unit can track the order progress in real time and notify the user. For example, it can send notifications to the user at each stage, such as when the order is accepted, when cooking begins, and when delivery begins, allowing them to wait with peace of mind. The ordering unit also automates the payment process, enabling users to complete payments smoothly. For example, it supports multiple payment methods, such as credit cards and electronic money, to enhance user convenience. This allows the ordering unit to efficiently manage the ordering process to provide users with healthy meals quickly and reliably, thereby improving user satisfaction.

[0068] The decision unit can determine a menu based on the user's health checkup information. For example, the decision unit may suggest a nutritionally balanced menu based on the user's health checkup information. For example, the decision unit may determine a menu considering the user's diagnosis results and the doctor's comments. The decision unit can also analyze the user's health checkup information in real time and generate data for appropriate menu suggestions. For example, the decision unit may suggest a menu fortified with specific nutrients based on the user's health checkup information. This allows for the determination of a more appropriate menu based on the user's health checkup information. Health checkup information includes, but is not limited to, diagnosis results and doctor's comments. Some or all of the above processing in the decision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision unit can input the user's health checkup information into a generation AI, and the generation AI can determine a menu.

[0069] The decision unit can determine a menu that accommodates special dietary requests. For example, the decision unit can suggest menus that address special dietary requests such as vegetarian, gluten-free, or low-carbohydrate diets. For instance, the decision unit considers the user's special dietary requests and determines an appropriate menu. The decision unit can also suggest menus using alternative ingredients to accommodate special dietary requests. For example, the decision unit might suggest a menu using gluten-free ingredients to a gluten-free user. This allows the decision unit to determine a menu that addresses special dietary requests. Special dietary requests include, but are not limited to, vegetarian, gluten-free, and low-carbohydrate diets. Some or all of the above processing in the decision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision unit can input the user's special dietary requests into a generative AI, which can then determine a menu.

[0070] The selection unit can select restaurants based on the user's location and delivery range. For example, the selection unit can select the optimal restaurant based on the user's location. For example, the selection unit can select a restaurant that offers healthy food in the area where the user lives. The selection unit can also select restaurants considering the delivery range. For example, the selection unit can select restaurants considering delivery ratings and delivery times. This allows the selection of the optimal restaurant to be made considering the user's location and delivery range. Location includes, but is not limited to, GPS data and address information. Delivery range includes, but is not limited to, distance and delivery time. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's location data into AI, and the AI ​​can select the optimal restaurant.

[0071] The ordering unit can automatically place orders with selected stores. For example, the ordering unit can automatically place orders with selected stores. For example, the ordering unit can automate the ordering process using API integration. The ordering unit is also designed so that orders can be completed without any special action by the user. For example, the ordering unit can automatically send orders to selected stores and arrange delivery. This allows for automatic ordering with selected stores. Methods for automatic ordering include, but are not limited to, API integration and automation of the ordering process. Some or all of the above processes in the ordering unit may or may not be performed using AI. For example, the ordering unit can input information about selected stores into AI, which can then automatically place orders.

[0072] The reception desk can estimate the user's emotions and adjust the input method for health status and dietary preferences based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of health status and dietary preferences. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0073] The reception desk can analyze the user's past health check data and provide the optimal input format. For example, the reception desk can automatically input the necessary items based on the user's past health check data. The reception desk can also suggest prioritizing the input of specific items based on the user's past health check data. Furthermore, the reception desk can analyze the user's past health check data and customize the input format. This allows the reception desk to provide the optimal input format based on the user's past health check data. Past health check data includes, but is not limited to, diagnostic history and test results. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health check data into an AI, which can then provide the optimal input format.

[0074] The reception unit can filter inputs regarding health status and dietary preferences, taking into account the user's lifestyle and exercise level. For example, the reception unit can filter input items based on the user's lifestyle data. The reception unit can also adjust input items considering the user's exercise level data. Furthermore, the reception unit can comprehensively analyze the user's lifestyle and exercise level and suggest the most suitable input items. This allows the system to filter input items considering the user's lifestyle and exercise level. Lifestyle includes, but is not limited to, eating patterns, sleep duration, and exercise habits. Exercise level includes, but is not limited to, steps taken, calories burned, and exercise time. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into AI, which can then filter the input items.

[0075] The reception desk can estimate the user's emotions and prioritize input data based on the estimated emotions. For example, if the user is stressed, the reception desk may suggest prioritizing the input of important data. If the user is relaxed, the reception desk may also suggest prioritizing the input of detailed data. Furthermore, if the user is in a hurry, the reception desk may suggest prioritizing the input of only the essential data. This allows for more appropriate data entry by prioritizing input data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0076] The reception desk can prioritize the acquisition of highly relevant data when users input their health status and dietary preferences, taking into account their geographical location. For example, the reception desk can prioritize acquiring region-specific health information based on the user's current location. It can also prioritize acquiring relevant food information, taking into account the user's geographical location. Furthermore, it can prioritize acquiring regional health trends based on the user's geographical location. This allows the reception desk to prioritize the acquisition of highly relevant data, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant data includes, but is not limited to, information on nearby stores and information on local food ingredients. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize the acquisition of highly relevant data.

[0077] The reception desk can analyze the user's social media activity and obtain relevant data when the user inputs their health status and dietary preferences. For example, the reception desk can analyze the user's social media posts and obtain data related to their health status and dietary preferences. The reception desk can also analyze posts from the user's social media followers and friends and obtain relevant data. Furthermore, the reception desk can obtain data related to their health status and dietary preferences based on the user's social media activity history. This allows the reception desk to analyze the user's social media activity and obtain relevant data. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then obtain relevant data.

[0078] The decision-making unit can estimate the user's emotions and adjust the menu presentation based on the estimated emotions. For example, if the user is relaxed, the decision-making unit can provide a detailed menu description. If the user is in a hurry, it can provide a concise menu description. Furthermore, if the user is excited, it can provide a visually appealing menu. By adjusting the menu presentation according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using or without a generative AI. For example, the decision-making unit can input user facial expression data into a generative AI, which can then estimate the emotions.

[0079] The decision-making unit can analyze the user's past eating history and suggest menus when determining a menu. For example, the decision-making unit can suggest a nutritionally balanced menu based on the user's past eating history. It can also suggest a menu that includes the user's preferred ingredients based on their past eating history. Furthermore, the decision-making unit can analyze the user's past eating history and suggest a menu that is appropriate for their health condition. In this way, it can analyze the user's past eating history and suggest the optimal menu. Past eating history includes, but is not limited to, meal records and order history. Some or all of the above processing in the decision-making unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision-making unit can input the user's past eating history into a generation AI, and the generation AI can suggest a menu.

[0080] The decision-making unit can customize the menu when determining the menu, taking into account the user's allergy information. For example, the decision-making unit can suggest a menu that does not contain allergens based on the user's allergy information. The decision-making unit can also suggest a menu that uses alternative ingredients, taking into account the user's allergy information. Furthermore, the decision-making unit can customize a menu that avoids allergens based on the user's allergy information. In this way, the menu can be customized taking into account the user's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergy. Some or all of the above processing in the decision-making unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the decision-making unit can input the user's allergy information into a generation AI, and the generation AI can customize the menu.

[0081] The decision-making unit can estimate the user's emotions and adjust the level of detail in the menu based on the estimated emotions. For example, if the user is relaxed, the decision-making unit can provide a detailed menu description. If the user is in a hurry, it can provide a concise menu description. Furthermore, if the user is excited, it can provide a visually appealing menu. By adjusting the level of detail in the menu according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using or without a generative AI. For example, the decision-making unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0082] The decision unit can determine the priority of menu items based on the user's meal timing when selecting a menu. For example, if the user requests breakfast, the decision unit will prioritize suggesting menu items suitable for breakfast. Similarly, if the user requests lunch, the decision unit can prioritize suggesting menu items suitable for lunch. Furthermore, if the user requests dinner, the decision unit can prioritize suggesting menu items suitable for dinner. This allows the decision unit to determine menu priorities based on the user's meal timing. Meal timing includes, but is not limited to, breakfast, lunch, and dinner times. Some or all of the above processing in the decision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision unit can input the user's meal timing data into a generative AI, which can then determine the menu priorities.

[0083] The decision-making unit can adjust the order of menu items based on the relevance of the user's meals when determining the menu. For example, the decision-making unit can suggest the next menu item based on the relevance of menu items the user has previously selected. The decision-making unit can also analyze the user's eating patterns and suggest highly relevant menu items. Furthermore, the decision-making unit can consider the user's eating preferences and suggest highly relevant menu items in order. This allows the order of menu items to be adjusted based on the relevance of the user's meals. Relevance of meals includes, but is not limited to, combinations of ingredients and nutritional balance. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the decision-making unit can input the user's meal relevance data into a generative AI, which can then adjust the order of the menu items.

[0084] The selection unit can estimate the user's emotions and adjust the store selection criteria based on the estimated emotions. For example, if the user is relaxed, the selection unit can provide detailed store information. If the user is in a hurry, the selection unit can also prioritize stores that can deliver quickly. Furthermore, if the user is excited, the selection unit can select visually appealing stores. In this way, by adjusting the store selection criteria according to the user's emotions, a more appropriate store can be selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0085] The selection unit can select a restaurant by analyzing the user's past order history. For example, the selection unit can select a restaurant based on the user's past order history. The selection unit can also select a high-quality restaurant from the user's past order history. Furthermore, the selection unit can analyze the user's past order history and select the restaurant with the highest satisfaction rate. In this way, the optimal restaurant can be selected by analyzing the user's past order history. Past order history includes, but is not limited to, order date and time, order details, and ratings. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past order history data into AI, and the AI ​​can select the optimal restaurant.

[0086] The selection unit can customize restaurants based on the user's food preferences when selecting a restaurant. For example, the selection unit can select restaurants that offer ingredients preferred by the user. It can also select restaurants that offer specific dishes based on the user's food preferences. Furthermore, the selection unit can select restaurants that offer customizable menus, taking into account the user's food preferences. This allows for the customization of restaurants based on the user's food preferences. Food preferences include, but are not limited to, favorite dishes, disliked dishes, and allergy information. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's food preference data into an AI, which can then customize the restaurants.

[0087] The selection unit can estimate the user's emotions and adjust the order in which it displays the store selection results based on the estimated emotions. For example, if the user is relaxed, the selection unit may prioritize displaying detailed store information. If the user is in a hurry, the selection unit may also prioritize displaying stores that can deliver quickly. Furthermore, if the user is excited, the selection unit may also prioritize displaying visually appealing stores. This allows for the selection of a more appropriate store by adjusting the order in which the store selection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0088] The selection unit can select restaurants considering the geographical distribution of users. For example, the selection unit can select the nearest restaurant based on the user's current location. It can also select restaurants within a delivery range, considering the user's geographical distribution. Furthermore, the selection unit can select restaurants that offer regionally specific cuisine based on the user's geographical distribution. This allows for the selection of the optimal restaurant, taking into account the user's geographical distribution. Geographical distribution includes, but is not limited to, user address information and GPS data. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user geographical distribution data into AI, which can then select the optimal restaurant.

[0089] The selection unit can improve the accuracy of its selections by referring to the user's relevant literature when selecting restaurants. For example, the selection unit can select restaurants that are considered healthy based on the user's relevant literature. The selection unit can also select restaurants that use specific ingredients by referring to the user's relevant literature. Furthermore, the selection unit can select restaurants that offer nutritionally balanced menus based on the user's relevant literature. This allows the selection unit to improve the accuracy of its selections by referring to the user's relevant literature. Relevant literature includes, but is not limited to, academic papers, technical reports, and patent documents. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's relevant literature data into AI, which can then improve the accuracy of its selections.

[0090] The ordering system can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is relaxed, the ordering system can provide detailed ordering options. If the user is in a hurry, it can also provide concise ordering options. Furthermore, if the user is excited, it can provide visually appealing ordering options. This allows for more appropriate ordering by adjusting the ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input user facial expression data into a generative AI, which can then estimate emotions.

[0091] The ordering department can analyze the user's past order history to select the optimal ordering method at the time of ordering. For example, the ordering department can suggest a preferred ordering method based on the user's past order history. The ordering department can also suggest a faster ordering method based on the user's past order history. Furthermore, the ordering department can analyze the user's past order history and suggest the ordering method that will provide the highest satisfaction. This allows the ordering department to analyze the user's past order history and select the optimal ordering method. Past order history includes, but is not limited to, order date and time, order details, and ratings. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's past order history data into AI, which can then select the optimal ordering method.

[0092] The ordering system can customize the ordering process based on the user's current circumstances at the time of ordering. For example, if the user is busy, the ordering system can provide a quick ordering method. Alternatively, if the user is relaxed, the ordering system can provide a detailed ordering method. Furthermore, the ordering system can suggest the most suitable ordering method considering the user's circumstances. This allows the ordering process to be customized based on the user's current circumstances. Current circumstances include, but are not limited to, work situation, family situation, and health status. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input the user's circumstances data into an AI, which can then suggest the most suitable ordering method.

[0093] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is in a hurry, the ordering system will process the order quickly. If the user is relaxed, the ordering system can also offer detailed ordering options. Furthermore, if the user is excited, the ordering system can offer visually appealing ordering options. This allows for more appropriate orders by prioritizing orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input user facial expression data into a generative AI, which can then estimate emotions.

[0094] The ordering department can select the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering department can suggest the fastest delivery method based on the user's current location. The ordering department can also set the optimal delivery range, taking into account the user's geographical location. Furthermore, the ordering department can suggest region-specific ordering methods based on the user's geographical location. This allows the ordering department to select the optimal ordering method, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's geographical location information into AI, which can then select the optimal ordering method.

[0095] The ordering department can analyze the user's social media activity and suggest ordering methods when an order is placed. For example, the ordering department can analyze the user's social media posts and suggest preferred ordering methods. It can also analyze posts from the user's social media followers and friends and suggest relevant ordering methods. Furthermore, the ordering department can suggest the optimal ordering method based on the user's social media activity history. This allows the system to analyze the user's social media activity and suggest the most suitable ordering method. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's social media data into an AI, which can then suggest the optimal ordering method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The healthy meal suggestion system can also acquire user exercise data and incorporate it into the suggested menus. For example, it can acquire data such as the type and frequency of exercise the user performs daily and the calories burned, and then suggest menus that require energy replenishment based on this data. It can also suggest recovery menus using ingredients rich in protein after exercise. Furthermore, based on exercise data, it can provide nutritionally balanced menus tailored to the user's exercise goals. This enables the suggestion of healthy meals that match the user's exercise habits, leading to more effective health management.

[0098] The healthy meal suggestion system can estimate the user's emotions and adjust menu suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest a menu using ingredients that have a relaxing effect. If the user is tired, it can suggest a menu suitable for replenishing energy. Furthermore, if the user is happy, it can even suggest a special dessert menu. This enables menu suggestions that respond to the user's emotions, supporting their emotional well-being through meals.

[0099] A healthy meal suggestion system can analyze a user's past eating history and reflect that in the suggested menus. For example, it can suggest similar menus based on dishes and ingredients the user has enjoyed eating in the past. It can also analyze nutritional imbalances from past eating history and suggest menus that supplement necessary nutrients. Furthermore, it can understand the user's eating patterns based on past eating history and suggest meals at appropriate times. This enables more personalized menu suggestions that utilize the user's past eating history.

[0100] The healthy meal suggestion system can estimate the user's emotions and adjust the delivery timing based on those emotions. For example, if the user is in a hurry, it can prioritize selecting restaurants that can deliver quickly. If the user is relaxed, it can select restaurants with more flexible delivery times. Furthermore, if the user is excited, it can suggest deliveries tailored to special events. By adjusting the delivery timing according to the user's emotions, it can provide a more satisfying service.

[0101] The healthy eating suggestion system can propose menus using local ingredients, taking into account the user's geographical location. For example, it can suggest menus using fresh ingredients harvested in the user's area. It can also suggest menus incorporating traditional local dishes and seasonal ingredients. Furthermore, it can suggest menus using ingredients directly sourced from local farmers and producers. This enables the provision of locally-rooted healthy eating suggestions that leverage the user's geographical location.

[0102] The healthy meal suggestion system can estimate the user's emotions and adjust the menu presentation based on those emotions. For example, if the user is relaxed, it can provide detailed menu descriptions and beautiful photos. If the user is in a hurry, it can provide concise menu descriptions and simple photos. Furthermore, if the user is excited, it can provide a visually appealing presentation. By adjusting the menu presentation according to the user's emotions, it becomes possible to provide more effective menu suggestions.

[0103] A healthy meal suggestion system can acquire users' lifestyle data and incorporate it into the suggested menus. For example, it can suggest menus using ingredients with relaxing effects based on the user's sleep patterns and stress levels. It can also suggest easy-to-prepare menus considering the user's work schedule and family situation. Furthermore, it can provide menus tailored to specific health goals based on the user's lifestyle data. This enables the suggestion of healthy meals that are tailored to the user's lifestyle, leading to more effective health management.

[0104] The healthy meal suggestion system can estimate the user's emotions and adjust the selection of ingredients based on those emotions. For example, if the user is stressed, it can suggest a menu using herbs and spices that have a relaxing effect. If the user is tired, it can suggest a menu using ingredients suitable for energy replenishment. Furthermore, if the user is happy, it can suggest a menu using special desserts or celebratory ingredients. By adjusting the selection of ingredients according to the user's emotions, it becomes possible to suggest menus that are more satisfying.

[0105] A healthy meal suggestion system can analyze users' social media activity and reflect it in the menu suggestions. For example, it can suggest menus based on photos and comments of meals shared by users on social media. It can also analyze posts from users' followers and friends to suggest menus that match current trends. Furthermore, it can suggest menus tailored to specific events or seasons based on the user's social media activity history. This enables more personalized menu suggestions that leverage users' social media activity.

[0106] The healthy meal suggestion system can estimate the user's emotions and adjust the delivery method based on those emotions. For example, if the user is relaxed, it can suggest a slow-paced delivery. If the user is in a hurry, it can suggest an expedited delivery. Furthermore, if the user is excited, it can suggest special delivery options. By adjusting the delivery method according to the user's emotions, it can provide a more satisfying service.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives the user's health status and dietary preferences. The user's health status includes, but is not limited to, blood pressure, blood sugar levels, and weight. Dietary preferences include, but are not limited to, favorite foods, disliked foods, and allergy information. The reception desk stores the health status and dietary preference information entered by the user in a database. The reception desk can also analyze the information entered by the user in real time and generate data for appropriate menu suggestions. For example, the reception desk analyzes trends in health status and dietary preferences based on the information entered by the user. Step 2: The decision unit uses a generation AI to determine the menu based on the information received by the reception unit. The generation AI considers the user's health condition and dietary preferences, for example, and proposes a nutritionally balanced menu. For example, the generation AI will suggest a low-sodium menu for a user with high blood pressure. It can also suggest a low-fat menu for a user with dyslipidemia. Furthermore, the generation AI can also propose menus that accommodate special dietary requests. For example, it can suggest menus that accommodate special dietary requests such as vegetarian, gluten-free, or low-carbohydrate options. Step 3: The selection unit selects restaurants to deliver based on the menu determined by the decision unit. The selection unit selects the most suitable restaurants by considering, for example, the user's location and the delivery area. For example, the selection unit selects restaurants that offer healthy food in the user's area. The selection unit can also select restaurants by considering factors such as delivery ratings and delivery time. Step 4: The ordering unit places an order for the menu determined by the decision unit from the restaurant selected by the selection unit. The ordering unit can, for example, automatically place an order with the selected restaurant. For example, the ordering unit can automate the ordering process using API integration. The ordering unit is also designed so that the user can complete the order without any special actions.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives the user's health status and food preferences. For example, the decision unit is implemented by the specific processing unit 290 of the data processing device 12, which determines the menu using a generating AI. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12, which selects a restaurant for delivery. For example, the ordering unit is implemented by the control unit 46A of the smart device 14, which automatically places an order with the selected restaurant. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which receives the user's health status and food preferences. For example, the decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines the menu using generating AI. For example, the selection unit is implemented by the identification processing unit 290 of the data processing device 12, which selects a restaurant for delivery. For example, the ordering unit is implemented by the control unit 46A of the smart glasses 214, which automatically places an order with the selected restaurant. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's health status and food preferences. For example, the decision unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the menu using a generation AI. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects a restaurant for delivery. For example, the ordering unit is implemented by the control unit 46A of the headset terminal 314 and automatically places an order with the selected restaurant. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the reception unit is implemented by the microphone 238 of the robot 414, which receives the user's health status and food preferences. For example, the decision unit is implemented by the specific processing unit 290 of the data processing device 12, which determines the menu using a generating AI. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12, which selects a restaurant for delivery. For example, the ordering unit is implemented by the control unit 46A of the robot 414, which automatically places an order with the selected restaurant. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception area that takes in the user's health status and dietary preferences, Based on the information received by the reception unit, a decision unit determines the menu, Based on the menu determined by the aforementioned determination unit, a selection unit selects a store to provide delivery, The system includes an ordering unit that orders the menu determined by the decision unit from the store selected by the selection unit. A system characterized by the following features. (Note 2) The aforementioned determination unit, The menu is determined based on the user's health checkup information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned determination unit, Determine a menu to accommodate special dietary requests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Stores are selected based on the user's location and delivery area. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering section is, Orders are automatically placed with the selected stores. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input methods for health status and dietary preferences based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past health checkup data and provide an input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their health status and dietary preferences, filtering is performed considering their lifestyle and exercise levels. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their health status or dietary preferences, the system prioritizes retrieving highly relevant data by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their health status and dietary preferences, the system analyzes their social media activity and obtains relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned determination unit, The system estimates the user's emotions and adjusts the menu's presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned determination unit, When deciding on a menu, the system analyzes the user's past meal history to suggest options. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned determination unit, When selecting a menu item, the menu is customized to take into account the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned determination unit, It estimates the user's emotions and adjusts the level of detail in the menu based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned determination unit, When selecting a menu, the menu priorities are determined based on the user's meal timing. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned determination unit, When selecting a menu, the order of the menu items is adjusted based on the relevance of the user's meals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is We estimate user sentiment and adjust store selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is When selecting a store, the system analyzes the user's past order history to make a selection. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When selecting a restaurant, customize the restaurant based on the user's dining preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is The system estimates the user's emotions and adjusts the order in which store selection results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is When selecting a store, consider the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When selecting a store, improve the accuracy of the selection based on the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ordering section is, It estimates the user's emotions and adjusts the ordering process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering section is, When an order is placed, the system analyzes the user's past order history to select the optimal ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering section is, When placing an order, the ordering method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, When an order is placed, the system selects the optimal ordering method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, When an order is placed, we analyze the user's social media activity and suggest ways to place the order. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 reception area that takes in the user's health status and dietary preferences, Based on the information received by the reception unit, a decision unit determines the menu, Based on the menu determined by the aforementioned determination unit, a selection unit selects a store to provide delivery, The system includes an ordering unit that orders the menu determined by the decision unit from the store selected by the selection unit. A system characterized by the following features.

2. The aforementioned determination unit, The menu is determined based on the user's health checkup information. The system according to feature 1.

3. The aforementioned determination unit, Determine a menu to accommodate special dietary requests. The system according to feature 1.

4. The aforementioned selection unit is Stores are selected based on the user's location and delivery area. The system according to feature 1.

5. The aforementioned ordering section is, Orders are automatically placed with the selected stores. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input methods for health status and dietary preferences based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past health checkup data and provide an input format. The system according to feature 1.

8. The aforementioned reception unit is When users input their health status and dietary preferences, filtering is performed considering their lifestyle and exercise levels. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input their health status or dietary preferences, the system prioritizes retrieving highly relevant data by considering their geographical location. The system according to feature 1.

Images