system

Abstract

The system according to this embodiment aims to efficiently create menus and procure ingredients according to the user's preferences. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a purchase unit, and a listing unit. The reception unit receives orders from users. The generation unit creates a menu plan based on the information received by the reception unit. The purchase unit automatically purchases ingredients that can be purchased online based on the menu plan created by the generation unit. The listing unit lists fresh foods that need to be purchased at a supermarket based on the menu plan created by the generation unit.

Description

Technical Field

[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 performed by at least one processor, the method including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=2l]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is time-consuming to create a menu and procure ingredients according to the user's preferences, and it is difficult to perform efficiently.

[0005] [[ID=三十九]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a purchasing unit, and a listing unit. The reception unit receives orders from users. The generation unit creates a menu plan based on the information received by the reception unit. The purchasing unit automatically purchases ingredients that can be purchased online based on the menu plan created by the generation unit. The listing unit lists fresh food items that need to be purchased at a supermarket based on the menu plan created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create menus and procure ingredients according to the user's 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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[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 reception 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 reception 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 menu agent system according to an embodiment of the present invention is a system that uses generating AI to automatically create menus tailored to the user's preferences and procure ingredients. This system works by the user inputting orders such as, "Create a month's worth of breakfast, lunch, and dinner menus. Mostly Chinese food, less seafood. Buy seasonings online. I'll buy fresh produce at the supermarket, so make a list of what I need," or "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list." The generating AI then collects and analyzes recipe information from the internet and product information from shopping sites to create a menu plan tailored to the user's preferences. Furthermore, the generating AI automatically purchases ingredients available online and lists any fresh produce that needs to be purchased at the supermarket. This mechanism allows the user to save time and effort in menu planning and ingredient purchasing. For example, the user inputs an order. At this time, the user only needs to input their preferences and requests specifically. For example, a user might input an order like, "Create a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Buy the seasonings online. I'll buy fresh produce at the supermarket, so make a list of what I need." This information is then input into the generating AI. Next, the generating AI analyzes the input information and collects and analyzes recipe information from the internet and product information from shopping sites. The generating AI creates a menu plan tailored to the user's preferences. For example, it might create a menu plan with more Chinese food and less seafood. The generating AI also automatically purchases seasonings and frozen foods that can be bought online and lists the fresh produce that needs to be purchased at the supermarket. The menu plan and ingredient list created by the generating AI are then provided to the user. The user can review the menu plan created by the generating AI and request revisions as needed. For example, they might request a revision like, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list." The generating AI recreates the menu plan in response to user requests for revisions and provides it to the user again.This system allows users to save time on meal planning and grocery shopping. For example, it can save busy working adults and housewives busy with childcare time on meal planning and grocery shopping. In addition, the generating AI creates meal plans tailored to the user's preferences, allowing users to enjoy meals that suit their tastes. Furthermore, the generating AI automatically purchases ingredients that can be bought online, saving users the trouble of shopping. For example, it can create meal plans that include junk food such as frozen fried rice and instant noodles, allowing users to enjoy not only nutritionally balanced meals but also junk food from time to time. In this way, the meal planning agent system saves users the trouble of meal planning and grocery shopping.

[0029] The menu agent system according to this embodiment comprises a reception unit, a generation unit, a purchase unit, and a listing unit. The reception unit receives orders from users. User orders include, but are not limited to, the type of meal, ingredient specifications, and allergy information. The reception unit accepts orders such as, "Make a menu for breakfast, lunch, and dinner for one month. Mostly Chinese food, with less seafood. Buy the seasonings online. I'll buy fresh food at the supermarket, so make a list of what I need." The generation unit uses a generation AI to create a menu plan based on the information received by the reception unit. The generation unit, for example, uses a generation AI to collect and analyze recipe information from the internet and create a menu plan tailored to the user's preferences. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate a menu plan tailored to the user's preferences. The generation unit can also use the generation AI to recreate the menu plan in response to user requests for revisions. For example, the generation unit receives a request for revisions from the user, such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, list some easy-to-eat items. I'll decide what to buy from that list." The generation AI then creates a new menu plan. The purchase unit automatically purchases ingredients that can be bought online based on the menu plan created by the generation unit. The purchase unit, for example, automatically purchases ingredients that can be bought online using the generation AI. The generation AI, for example, collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. The listing unit lists fresh foods that need to be purchased at the supermarket based on the menu plan created by the generation unit. The listing unit, for example, lists fresh foods that need to be purchased at the supermarket using the generation AI. The generation AI, for example, lists fresh foods according to the user's preferences and provides them to the user. In this way, the menu agent system according to the embodiment can reduce the user's workload by automating everything from menu creation to ingredient procurement based on the user's order.

[0030] The reception desk receives user orders. User orders include, but are not limited to, the type of meal, ingredient specifications, and allergy information. For example, the reception desk accepts orders such as, "Please prepare a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Please buy the seasonings online. I'll buy fresh produce at the supermarket, so please make a list of what I need." Specifically, the reception desk receives information entered by users via smartphones or computers and processes it appropriately within the system. Users can easily enter detailed orders using a dedicated application or web interface. For example, when entering allergy information, users can not only select specific ingredients but also enter the severity of allergies and a history of past allergic reactions. This allows the system to suggest menus that are best suited to the user's health condition. The reception desk also collects information about the user's dietary preferences and lifestyle. For example, if a user is a vegetarian or follows a specific diet, the menu can be customized based on that information. Furthermore, the reception desk can provide more personalized service by referring to the user's past order history and learning their preferences and tendencies. This allows the reception desk to address the diverse needs of users and build a foundation for providing optimal menu plans for each individual user.

[0031] The generation unit uses a generation AI to create menu plans based on information received by the reception unit. For example, the generation unit's generation AI collects and analyzes recipe information from the internet to create menu plans tailored to the user's preferences. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate menu plans tailored to the user's preferences. Specifically, the generation AI refers to a large recipe database and selects the most suitable recipes based on the user's order. For example, if the order is for more Chinese food, it will prioritize Chinese recipes and include many recipes that do not contain seafood to reduce the frequency of seafood. The generation AI also considers the user's allergy information and selects recipes that do not contain ingredients that may cause allergies. Furthermore, the generation unit customizes the menu plan according to the user's lifestyle and dietary preferences. For example, it will suggest recipes that are quick and easy to prepare for busy users, and nutritionally balanced recipes for health-conscious users. The generation AI can also continuously improve the menu plan based on user feedback. For example, the system can receive a request for modifications such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items, and then decide what to buy from that list." The generation AI then creates a new meal plan. This allows the generation unit to respond to the diverse needs of users and provide the most suitable meal plan.

[0032] The purchasing unit automatically purchases ingredients available online based on the menu plan created by the generation unit. For example, the generation AI automatically purchases ingredients available online. The generation AI collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. Specifically, the generation AI lists the necessary ingredients based on the user's order, compares prices and stock availability from various shopping sites, and selects the best supplier. For example, seasonings and non-perishable ingredients are purchased from online shops that offer low prices and fast delivery. The generation AI can also refer to the user's past purchase history and prioritize brands and products preferred by the user. Furthermore, the purchasing unit reduces user effort by automating the purchase process. For example, the generation AI uses the user's account information to log in to shopping sites, add necessary ingredients to the cart, and complete the payment process. This allows the user to purchase the necessary ingredients without any hassle. The purchasing unit can also track the delivery status of purchased ingredients and notify the user. For example, by informing users of the scheduled delivery date and delivery status, users can confirm the arrival of ingredients and proceed with cooking in a planned manner. This allows the purchasing department to efficiently support users in procuring ingredients and improve the overall convenience of the menu agent system.

[0033] The listing unit lists fresh produce items that need to be purchased at the supermarket based on the menu plan created by the generation unit. For example, the generation AI lists the fresh produce items that need to be purchased at the supermarket. The generation AI also lists fresh produce items that are tailored to the user's preferences and provides them to the user. Specifically, the generation AI identifies and lists the necessary fresh produce items based on the menu plan. For example, it lists fresh produce items that need to be purchased at the supermarket, such as vegetables, fruits, meat, and seafood. The generation AI also considers the user's preferences and allergy information to select appropriate fresh produce items. Furthermore, the listing unit can also provide information on where to buy the listed fresh produce items and their prices. For example, it suggests the best place to buy based on sale information from nearby supermarkets or price information from supermarkets the user frequently uses. This allows the user to purchase fresh produce items efficiently. In addition, the listing unit can continuously improve the list content based on user feedback. For example, if a user prefers certain fresh foods, those foods will be prioritized in the list, while foods the user wants to avoid will be excluded. This allows the listing system to provide an optimal list of fresh foods tailored to the user's preferences, supporting their grocery shopping. Furthermore, the listing system synchronizes the list content with smartphones and computers, allowing users to access the list anytime. This enables users to refer to the list while shopping and ensure they purchase all necessary fresh foods.

[0034] The generation unit can create menu plans tailored to the user's preferences using a generation AI. For example, if the user inputs a request such as "more Chinese food and less seafood," the generation AI will collect and analyze recipe information from the internet and create a menu plan that includes more Chinese food and less seafood. Some or all of the above processing in the generation unit is performed using the generation AI. As a result, by using the generation AI, menu plans tailored to the user's preferences can be automatically created.

[0035] The generation unit can collect and analyze recipe information from the internet using a generation AI and create menu plans tailored to the user's preferences. For example, the generation unit uses a generation AI to collect and analyze recipe information from the internet and create menu plans tailored to the user's preferences. The generation AI uses, for example, web scraping technology to collect recipe information from the internet and natural language processing technology to analyze the recipe information. Some or all of the above processing in the generation unit is performed using the generation AI. As a result, by collecting and analyzing recipe information from the internet, it is possible to provide a wider variety of menu plans.

[0036] The purchasing unit can automatically purchase groceries available online using a generating AI. For example, the generating AI automatically purchases groceries available online. The generating AI, for example, collects product information from shopping sites and automatically purchases groceries according to the user's preferences. Some or all of the above processes in the purchasing unit are performed using the generating AI. This reduces the user's shopping effort by automatically purchasing groceries available online.

[0037] The listing unit can use a generating AI to list fresh food items that need to be purchased at the supermarket. For example, the generating AI lists fresh food items that need to be purchased at the supermarket. The generating AI also lists fresh food items according to the user's preferences and provides them to the user. Some or all of the above processing in the listing unit is performed using the generating AI. This allows users to shop efficiently by listing the fresh food items that need to be purchased at the supermarket.

[0038] The generation unit can recreate the menu plan in response to user revision requests using a generation AI. For example, if a user requests a revision such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list," the generation AI will collect and analyze internet recipe information again and create a revised menu plan. Some or all of the above processing in the generation unit is performed using the generation AI. This allows for flexible responses to user requests by recreating the menu plan in response to user revision requests.

[0039] The reception desk can analyze the user's past order history and select the optimal reception method. For example, the reception desk can prioritize suggesting menus that the user has frequently ordered in the past. The reception desk can also suggest menus suitable for a specific time of day based on the user's past order history. The reception desk can also analyze the user's preferences based on their past order history and make suggestions accordingly. In this way, the optimal reception method can be selected by analyzing the user's past order history. Some or all of the above processes in the reception desk may be performed using AI, or they may not be performed using AI.

[0040] The reception desk can filter orders based on the user's current dietary preferences and health status. For example, if the user is health-conscious, the reception desk will prioritize suggesting nutritionally balanced menus. If the user wishes to avoid a particular ingredient, the reception desk can also suggest menus that do not include that ingredient. If the user is on a specific diet, the reception desk can also suggest menus suitable for that diet. In this way, by filtering based on the user's current dietary preferences and health status, more appropriate menus can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not.

[0041] The reception desk can prioritize orders based on the user's geographical location when receiving an order. For example, if the user is in a specific region, the reception desk can prioritize suggesting ingredients available in that region. If the user is traveling, the reception desk can also suggest ingredients available at their travel destination. If the user is at home, the reception desk can also suggest ingredients available around their home. This allows the reception desk to prioritize orders based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0042] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. For example, the reception desk can prioritize suggesting ingredients that the user is talking about on social media. The reception desk can also suggest menus based on recipes that the user has shared on social media. The reception desk can also suggest ingredients recommended by the user's social media followers. In this way, relevant orders can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0043] The generation unit can adjust the level of detail in the menu based on the importance of the ingredients when generating a menu plan. For example, the generation unit can provide detailed cooking methods for major ingredients. The generation unit can also provide concise descriptions for auxiliary ingredients. If a particular ingredient is important, the generation unit can also construct the menu around that ingredient. This allows for the provision of more appropriate menu plans by adjusting the level of detail based on the importance of the ingredients. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0044] The generation unit can apply different generation algorithms depending on the category of cuisine when generating menu plans. For example, in the case of Chinese cuisine, the generation unit can generate menus based on specific seasonings and cooking methods. In the case of Japanese cuisine, the generation unit can also generate menus based on traditional cooking methods. In the case of Western cuisine, the generation unit can also generate menus based on modern cooking methods. This allows for the provision of a wider variety of menu plans by applying different generation algorithms depending on the category of cuisine. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0045] The generation unit can determine the priority of menus based on the availability of ingredients when generating menu plans. For example, the generation unit can propose menus that prioritize the use of seasonal ingredients. The generation unit can also propose menus that avoid ingredients that are difficult to obtain. The generation unit can also propose menus that use ingredients that are only available at specific times of the year. By determining the priority of menus based on the availability of ingredients, it is possible to provide more appropriate menu plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0046] The generation unit can adjust the order of menus based on the relationships between ingredients when generating a menu plan. For example, the generation unit can propose menus that use the same ingredients consecutively. The generation unit can also determine the order of menus by considering the shelf life of the ingredients. The generation unit can also adjust the order of menus based on the cooking methods of the ingredients. By adjusting the order of menus based on the relationships between ingredients, a more appropriate menu plan can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0047] The purchasing department can improve the accuracy of purchases by considering the interrelationships of ingredients at the time of purchase. For example, the purchasing department can purchase ingredients used in the same dish together. The purchasing department can also purchase ingredients considering their shelf life. The purchasing department can also purchase ingredients based on how they are cooked. By considering the interrelationships of ingredients, it is possible to purchase more appropriate ingredients. Some or all of the above processing in the purchasing department may be performed using AI or not.

[0048] The purchasing department can make purchases while considering the attribute information of the food supplier. For example, the purchasing department can prioritize the purchase of organic food. The purchasing department can also prioritize the purchase of local food. The purchasing department can also prioritize the purchase of food from a specific brand. By considering the attribute information of the food supplier, it is possible to purchase more appropriate food. Some or all of the above processing in the purchasing department may be performed using AI or not.

[0049] The purchasing department can consider the geographical distribution of ingredients when making purchases. For example, the purchasing department can prioritize purchasing local ingredients. The purchasing department can also purchase specialty products from specific regions. The purchasing department can also avoid purchasing imported ingredients. By considering the geographical distribution of ingredients, it is possible to purchase more appropriate ingredients. Some or all of the above processing in the purchasing department may be performed using AI, or not.

[0050] The purchasing department can improve the accuracy of its purchases by referring to relevant literature on ingredients during the purchasing process. For example, the purchasing department can refer to the nutritional information of ingredients before purchasing. The purchasing department can also refer to the cooking methods of ingredients before purchasing. The purchasing department can also refer to the storage methods of ingredients before purchasing. This allows for the purchase of more appropriate ingredients by referring to relevant literature on ingredients. Some or all of the above processes in the purchasing department may be performed using AI, or they may not be performed using AI.

[0051] The listing unit can improve the accuracy of the listing by considering the interrelationships of ingredients during the listing process. For example, the listing unit can group together ingredients used in the same dish. The listing unit can also list ingredients considering their shelf life. The listing unit can also list ingredients based on their cooking method. This allows for the listing of more appropriate ingredients by considering the interrelationships of ingredients. Some or all of the above processing in the listing unit may be performed using AI, or it may be performed without using AI.

[0052] The listing unit can perform listing while considering the attribute information of the food supplier. For example, the listing unit can prioritize listing organic food. The listing unit can also prioritize listing local food. The listing unit can also prioritize listing food from a specific brand. This allows for the listing of more appropriate food by considering the attribute information of the food supplier. Some or all of the above processing in the listing unit may be performed using AI or not.

[0053] The listing unit can consider the geographical distribution of ingredients when listing them. For example, the listing unit can prioritize listing local ingredients. The listing unit can also list specialty products from specific regions. The listing unit can also avoid listing imported ingredients. This allows for the listing of more appropriate ingredients by considering the geographical distribution of ingredients. Some or all of the above processing in the listing unit may be performed using AI or not.

[0054] The listing unit can improve the accuracy of its listings by referring to relevant literature on ingredients during the listing process. For example, the listing unit can list ingredients by referring to their nutritional information. The listing unit can also list ingredients by referring to their cooking methods. The listing unit can also list ingredients by referring to their storage methods. This allows for the listing of more appropriate ingredients by referring to relevant literature on ingredients. Some or all of the above processing in the listing unit may be performed using AI, or it may be performed without using AI.

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

[0056] The reception desk can analyze a user's past order history and select the optimal reception method. For example, it can prioritize suggesting menus that the user has frequently ordered in the past. It can also suggest menus suitable for specific time slots based on the user's past order history. It can also analyze and suggest preferences based on the user's past order history. In this way, the optimal reception method can be selected by analyzing the user's past order history. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0057] The reception desk can filter orders based on the user's current dietary preferences and health status. For example, if a user is health-conscious, it can prioritize suggesting nutritionally balanced menus. If a user wants to avoid a particular ingredient, it can suggest menus that do not contain that ingredient. If a user is on a specific diet, it can suggest menus suitable for that diet. In this way, by filtering based on the user's current dietary preferences and health status, more appropriate menus can be suggested. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0058] The reception desk can prioritize orders based on the user's geographical location when receiving an order. For example, if a user is in a specific region, it can prioritize suggesting ingredients available in that region. If a user is traveling, it can also suggest ingredients available at their travel destination. If a user is at home, it can also suggest ingredients available around their home. This allows for prioritizing orders based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0059] The generation unit can adjust the level of detail in the menu based on the importance of the ingredients when generating a menu plan. For example, it can provide detailed cooking methods for major ingredients, and brief descriptions for auxiliary ingredients. If a particular ingredient is important, the menu can be built around that ingredient. By adjusting the level of detail in the menu based on the importance of the ingredients, a more appropriate menu plan can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0060] The generation unit can apply different generation algorithms depending on the category of cuisine when generating menu plans. For example, in the case of Chinese cuisine, menus can be generated based on specific seasonings and cooking methods. In the case of Japanese cuisine, menus can be generated based on traditional cooking methods. In the case of Western cuisine, menus can be generated based on modern cooking methods. By applying different generation algorithms depending on the category of cuisine, a wider variety of menu plans can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0061] The generation unit can determine the priority of menus based on the availability of ingredients when generating menu plans. For example, it can propose menus that prioritize the use of seasonal ingredients. It can also propose menus that avoid ingredients that are difficult to obtain. It can also propose menus that use ingredients that are only available at specific times of the year. By determining the priority of menus based on the availability of ingredients, it is possible to provide more appropriate menu plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives the user's order. The user's order includes the type of meal, ingredient specifications, allergy information, etc. For example, it accepts an order from a user saying, "Please prepare a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Please buy the seasonings online. I'll buy the fresh food at the supermarket, so please make a list of what I need." Step 2: The generation unit uses a generation AI to create a menu plan based on the information received by the reception unit. The generation unit collects and analyzes recipe information from the internet to create a menu plan tailored to the user's preferences. The generation unit can also recreate the menu plan in response to user revision requests. For example, if a user requests a revision such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items, and then decide what to buy from that list," the generation AI will create a new menu plan. Step 3: The purchasing unit automatically purchases ingredients available online based on the menu plan created by the generation unit. The purchasing unit automatically purchases ingredients available online from the generation AI. The generation AI collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. Step 4: The listing unit lists the fresh food items that need to be purchased at the supermarket based on the menu plan created by the generation unit. The listing unit uses the generation AI to list the fresh food items that need to be purchased at the supermarket. The generation AI lists fresh food items according to the user's preferences and provides them to the user.

[0064] (Example of form 2) The menu agent system according to an embodiment of the present invention is a system that uses generating AI to automatically create menus tailored to the user's preferences and procure ingredients. This system works by the user inputting orders such as, "Create a month's worth of breakfast, lunch, and dinner menus. Mostly Chinese food, less seafood. Buy seasonings online. I'll buy fresh produce at the supermarket, so make a list of what I need," or "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list." The generating AI then collects and analyzes recipe information from the internet and product information from shopping sites to create a menu plan tailored to the user's preferences. Furthermore, the generating AI automatically purchases ingredients available online and lists any fresh produce that needs to be purchased at the supermarket. This mechanism allows the user to save time and effort in menu planning and ingredient purchasing. For example, the user inputs an order. At this time, the user only needs to input their preferences and requests specifically. For example, a user might input an order like, "Create a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Buy the seasonings online. I'll buy fresh produce at the supermarket, so make a list of what I need." This information is then input into the generating AI. Next, the generating AI analyzes the input information and collects and analyzes recipe information from the internet and product information from shopping sites. The generating AI creates a menu plan tailored to the user's preferences. For example, it might create a menu plan with more Chinese food and less seafood. The generating AI also automatically purchases seasonings and frozen foods that can be bought online and lists the fresh produce that needs to be purchased at the supermarket. The menu plan and ingredient list created by the generating AI are then provided to the user. The user can review the menu plan created by the generating AI and request revisions as needed. For example, they might request a revision like, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list." The generating AI recreates the menu plan in response to user requests for revisions and provides it to the user again.This system allows users to save time on meal planning and grocery shopping. For example, it can save busy working adults and housewives busy with childcare time on meal planning and grocery shopping. In addition, the generating AI creates meal plans tailored to the user's preferences, allowing users to enjoy meals that suit their tastes. Furthermore, the generating AI automatically purchases ingredients that can be bought online, saving users the trouble of shopping. For example, it can create meal plans that include junk food such as frozen fried rice and instant noodles, allowing users to enjoy not only nutritionally balanced meals but also junk food from time to time. In this way, the meal planning agent system saves users the trouble of meal planning and grocery shopping.

[0065] The menu agent system according to this embodiment comprises a reception unit, a generation unit, a purchase unit, and a listing unit. The reception unit receives orders from users. User orders include, but are not limited to, the type of meal, ingredient specifications, and allergy information. The reception unit accepts orders such as, "Make a menu for breakfast, lunch, and dinner for one month. Mostly Chinese food, with less seafood. Buy the seasonings online. I'll buy fresh food at the supermarket, so make a list of what I need." The generation unit uses a generation AI to create a menu plan based on the information received by the reception unit. The generation unit, for example, uses a generation AI to collect and analyze recipe information from the internet and create a menu plan tailored to the user's preferences. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate a menu plan tailored to the user's preferences. The generation unit can also use the generation AI to recreate the menu plan in response to user requests for revisions. For example, the generation unit receives a request for revisions from the user, such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, list some easy-to-eat items. I'll decide what to buy from that list." The generation AI then creates a new menu plan. The purchase unit automatically purchases ingredients that can be bought online based on the menu plan created by the generation unit. The purchase unit, for example, automatically purchases ingredients that can be bought online using the generation AI. The generation AI, for example, collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. The listing unit lists fresh foods that need to be purchased at the supermarket based on the menu plan created by the generation unit. The listing unit, for example, lists fresh foods that need to be purchased at the supermarket using the generation AI. The generation AI, for example, lists fresh foods according to the user's preferences and provides them to the user. In this way, the menu agent system according to the embodiment can reduce the user's workload by automating everything from menu creation to ingredient procurement based on the user's order.

[0066] The reception desk receives user orders. User orders include, but are not limited to, the type of meal, ingredient specifications, and allergy information. For example, the reception desk accepts orders such as, "Please prepare a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Please buy the seasonings online. I'll buy fresh produce at the supermarket, so please make a list of what I need." Specifically, the reception desk receives information entered by users via smartphones or computers and processes it appropriately within the system. Users can easily enter detailed orders using a dedicated application or web interface. For example, when entering allergy information, users can not only select specific ingredients but also enter the severity of allergies and a history of past allergic reactions. This allows the system to suggest menus that are best suited to the user's health condition. The reception desk also collects information about the user's dietary preferences and lifestyle. For example, if a user is a vegetarian or follows a specific diet, the menu can be customized based on that information. Furthermore, the reception desk can provide more personalized service by referring to the user's past order history and learning their preferences and tendencies. This allows the reception desk to address the diverse needs of users and build a foundation for providing optimal menu plans for each individual user.

[0067] The generation unit uses a generation AI to create menu plans based on information received by the reception unit. For example, the generation unit's generation AI collects and analyzes recipe information from the internet to create menu plans tailored to the user's preferences. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate menu plans tailored to the user's preferences. Specifically, the generation AI refers to a large recipe database and selects the most suitable recipes based on the user's order. For example, if the order is for more Chinese food, it will prioritize Chinese recipes and include many recipes that do not contain seafood to reduce the frequency of seafood. The generation AI also considers the user's allergy information and selects recipes that do not contain ingredients that may cause allergies. Furthermore, the generation unit customizes the menu plan according to the user's lifestyle and dietary preferences. For example, it will suggest recipes that are quick and easy to prepare for busy users, and nutritionally balanced recipes for health-conscious users. The generation AI can also continuously improve the menu plan based on user feedback. For example, the system can receive a request for modifications such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items, and then decide what to buy from that list." The generation AI then creates a new meal plan. This allows the generation unit to respond to the diverse needs of users and provide the most suitable meal plan.

[0068] The purchasing unit automatically purchases ingredients available online based on the menu plan created by the generation unit. For example, the generation AI automatically purchases ingredients available online. The generation AI collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. Specifically, the generation AI lists the necessary ingredients based on the user's order, compares prices and stock availability from various shopping sites, and selects the best supplier. For example, seasonings and non-perishable ingredients are purchased from online shops that offer low prices and fast delivery. The generation AI can also refer to the user's past purchase history and prioritize brands and products preferred by the user. Furthermore, the purchasing unit reduces user effort by automating the purchase process. For example, the generation AI uses the user's account information to log in to shopping sites, add necessary ingredients to the cart, and complete the payment process. This allows the user to purchase the necessary ingredients without any hassle. The purchasing unit can also track the delivery status of purchased ingredients and notify the user. For example, by informing users of the scheduled delivery date and delivery status, users can confirm the arrival of ingredients and proceed with cooking in a planned manner. This allows the purchasing department to efficiently support users in procuring ingredients and improve the overall convenience of the menu agent system.

[0069] The listing unit lists fresh produce items that need to be purchased at the supermarket based on the menu plan created by the generation unit. For example, the generation AI lists the fresh produce items that need to be purchased at the supermarket. The generation AI also lists fresh produce items that are tailored to the user's preferences and provides them to the user. Specifically, the generation AI identifies and lists the necessary fresh produce items based on the menu plan. For example, it lists fresh produce items that need to be purchased at the supermarket, such as vegetables, fruits, meat, and seafood. The generation AI also considers the user's preferences and allergy information to select appropriate fresh produce items. Furthermore, the listing unit can also provide information on where to buy the listed fresh produce items and their prices. For example, it suggests the best place to buy based on sale information from nearby supermarkets or price information from supermarkets the user frequently uses. This allows the user to purchase fresh produce items efficiently. In addition, the listing unit can continuously improve the list content based on user feedback. For example, if a user prefers certain fresh foods, those foods will be prioritized in the list, while foods the user wants to avoid will be excluded. This allows the listing system to provide an optimal list of fresh foods tailored to the user's preferences, supporting their grocery shopping. Furthermore, the listing system synchronizes the list content with smartphones and computers, allowing users to access the list anytime. This enables users to refer to the list while shopping and ensure they purchase all necessary fresh foods.

[0070] The generation unit can create menu plans tailored to the user's preferences using a generation AI. For example, if the user inputs a request such as "more Chinese food and less seafood," the generation AI will collect and analyze recipe information from the internet and create a menu plan that includes more Chinese food and less seafood. Some or all of the above processing in the generation unit is performed using the generation AI. As a result, by using the generation AI, menu plans tailored to the user's preferences can be automatically created.

[0071] The generation unit can collect and analyze recipe information from the internet using a generation AI and create menu plans tailored to the user's preferences. For example, the generation unit uses a generation AI to collect and analyze recipe information from the internet and create menu plans tailored to the user's preferences. The generation AI uses, for example, web scraping technology to collect recipe information from the internet and natural language processing technology to analyze the recipe information. Some or all of the above processing in the generation unit is performed using the generation AI. As a result, by collecting and analyzing recipe information from the internet, it is possible to provide a wider variety of menu plans.

[0072] The purchasing unit can automatically purchase groceries available online using a generating AI. For example, the generating AI automatically purchases groceries available online. The generating AI, for example, collects product information from shopping sites and automatically purchases groceries according to the user's preferences. Some or all of the above processes in the purchasing unit are performed using the generating AI. This reduces the user's shopping effort by automatically purchasing groceries available online.

[0073] The listing unit can use a generating AI to list fresh food items that need to be purchased at the supermarket. For example, the generating AI lists fresh food items that need to be purchased at the supermarket. The generating AI also lists fresh food items according to the user's preferences and provides them to the user. Some or all of the above processing in the listing unit is performed using the generating AI. This allows users to shop efficiently by listing the fresh food items that need to be purchased at the supermarket.

[0074] The generation unit can recreate the menu plan in response to user revision requests using a generation AI. For example, if a user requests a revision such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items. I'll decide what to buy from that list," the generation AI will collect and analyze internet recipe information again and create a revised menu plan. Some or all of the above processing in the generation unit is performed using the generation AI. This allows for flexible responses to user requests by recreating the menu plan in response to user revision requests.

[0075] The reception desk can estimate the user's emotions and adjust the timing of order acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk may accept the order quickly. If the user is relaxed, the reception desk may also accept the order after confirming the details. If the user is in a hurry, the reception desk may also provide a simplified order acceptance procedure. This allows for order acceptance at a more appropriate time by adjusting the timing of order acceptance 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0076] The reception desk can analyze the user's past order history and select the optimal reception method. For example, the reception desk can prioritize suggesting menus that the user has frequently ordered in the past. The reception desk can also suggest menus suitable for a specific time of day based on the user's past order history. The reception desk can also analyze the user's preferences based on their past order history and make suggestions accordingly. In this way, the optimal reception method can be selected by analyzing the user's past order history. Some or all of the above processes in the reception desk may be performed using AI, or they may not be performed using AI.

[0077] The reception desk can filter orders based on the user's current dietary preferences and health status. For example, if the user is health-conscious, the reception desk will prioritize suggesting nutritionally balanced menus. If the user wishes to avoid a particular ingredient, the reception desk can also suggest menus that do not include that ingredient. If the user is on a specific diet, the reception desk can also suggest menus suitable for that diet. In this way, by filtering based on the user's current dietary preferences and health status, more appropriate menus can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not.

[0078] The reception desk can estimate the user's emotions and determine the priority of orders to be received based on the estimated emotions. For example, if the user is in a hurry, the reception desk will process the order with the highest priority. If the user is relaxed, the reception desk can process the order in a balanced manner with other orders. If the user is stressed, the reception desk can process the order quickly. This allows orders to be processed in a more appropriate order by determining the priority of orders 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0079] The reception desk can prioritize orders based on the user's geographical location when receiving an order. For example, if the user is in a specific region, the reception desk can prioritize suggesting ingredients available in that region. If the user is traveling, the reception desk can also suggest ingredients available at their travel destination. If the user is at home, the reception desk can also suggest ingredients available around their home. This allows the reception desk to prioritize orders based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0080] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. For example, the reception desk can prioritize suggesting ingredients that the user is talking about on social media. The reception desk can also suggest menus based on recipes that the user has shared on social media. The reception desk can also suggest ingredients recommended by the user's social media followers. In this way, relevant orders can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0081] The generation unit can estimate the user's emotions and adjust the presentation of the menu plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a menu plan with detailed explanations. If the user is in a hurry, the generation unit can also provide a concise menu plan. If the user is excited, the generation unit can also provide a visually appealing menu plan. This allows for the provision of a more appropriate menu plan by adjusting the presentation of the menu plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using a generative AI.

[0082] The generation unit can adjust the level of detail in the menu based on the importance of the ingredients when generating a menu plan. For example, the generation unit can provide detailed cooking methods for major ingredients. The generation unit can also provide concise descriptions for auxiliary ingredients. If a particular ingredient is important, the generation unit can also construct the menu around that ingredient. This allows for the provision of more appropriate menu plans by adjusting the level of detail based on the importance of the ingredients. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0083] The generation unit can apply different generation algorithms depending on the category of cuisine when generating menu plans. For example, in the case of Chinese cuisine, the generation unit can generate menus based on specific seasonings and cooking methods. In the case of Japanese cuisine, the generation unit can also generate menus based on traditional cooking methods. In the case of Western cuisine, the generation unit can also generate menus based on modern cooking methods. This allows for the provision of a wider variety of menu plans by applying different generation algorithms depending on the category of cuisine. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0084] The generation unit can estimate the user's emotions and adjust the length of the meal plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short meal plan. If the user is relaxed, the generation unit can also provide a long meal plan. If the user is excited, the generation unit can also provide a varied meal plan. By adjusting the length of the meal plan according to the user's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using a generation AI.

[0085] The generation unit can determine the priority of menus based on the availability of ingredients when generating menu plans. For example, the generation unit can propose menus that prioritize the use of seasonal ingredients. The generation unit can also propose menus that avoid ingredients that are difficult to obtain. The generation unit can also propose menus that use ingredients that are only available at specific times of the year. By determining the priority of menus based on the availability of ingredients, it is possible to provide more appropriate menu plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0086] The generation unit can adjust the order of menus based on the relationships between ingredients when generating a menu plan. For example, the generation unit can propose menus that use the same ingredients consecutively. The generation unit can also determine the order of menus by considering the shelf life of the ingredients. The generation unit can also adjust the order of menus based on the cooking methods of the ingredients. By adjusting the order of menus based on the relationships between ingredients, a more appropriate menu plan can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0087] The purchasing unit can estimate the user's emotions and determine the priority of ingredients to purchase based on those emotions. For example, if the user is in a hurry, the purchasing unit will prioritize ingredients that can be purchased immediately. If the user is relaxed, the purchasing unit may allow the user to carefully select specific ingredients. If the user is stressed, the purchasing unit may also prioritize ingredients that are easy to cook. This allows for the purchase of more appropriate ingredients by prioritizing ingredients 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using AI or not.

[0088] The purchasing department can improve the accuracy of purchases by considering the interrelationships of ingredients at the time of purchase. For example, the purchasing department can purchase ingredients used in the same dish together. The purchasing department can also purchase ingredients considering their shelf life. The purchasing department can also purchase ingredients based on how they are cooked. By considering the interrelationships of ingredients, it is possible to purchase more appropriate ingredients. Some or all of the above processing in the purchasing department may be performed using AI or not.

[0089] The purchasing department can make purchases while considering the attribute information of the food supplier. For example, the purchasing department can prioritize the purchase of organic food. The purchasing department can also prioritize the purchase of local food. The purchasing department can also prioritize the purchase of food from a specific brand. By considering the attribute information of the food supplier, it is possible to purchase more appropriate food. Some or all of the above processing in the purchasing department may be performed using AI or not.

[0090] The purchasing section can estimate the user's emotions and adjust how the ingredients are displayed based on the estimated emotions. For example, if the user is relaxed, the purchasing section can display detailed information. If the user is in a hurry, the purchasing section can also display concise information. If the user is excited, the purchasing section can also provide a visually appealing display. This allows for the provision of more appropriate information by adjusting how ingredients 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing section may be performed using AI or not.

[0091] The purchasing department can consider the geographical distribution of ingredients when making purchases. For example, the purchasing department can prioritize purchasing local ingredients. The purchasing department can also purchase specialty products from specific regions. The purchasing department can also avoid purchasing imported ingredients. By considering the geographical distribution of ingredients, it is possible to purchase more appropriate ingredients. Some or all of the above processing in the purchasing department may be performed using AI, or not.

[0092] The purchasing department can improve the accuracy of its purchases by referring to relevant literature on ingredients during the purchasing process. For example, the purchasing department can refer to the nutritional information of ingredients before purchasing. The purchasing department can also refer to the cooking methods of ingredients before purchasing. The purchasing department can also refer to the storage methods of ingredients before purchasing. This allows for the purchase of more appropriate ingredients by referring to relevant literature on ingredients. Some or all of the above processes in the purchasing department may be performed using AI, or they may not be performed using AI.

[0093] The listing unit can estimate the user's emotions and determine the priority of the ingredients to list based on the estimated emotions. For example, if the user is in a hurry, the listing unit will prioritize ingredients that can be purchased immediately. If the user is relaxed, the listing unit can also allow the user to carefully select specific ingredients. If the user is stressed, the listing unit can also prioritize ingredients that are easy to cook. In this way, by determining the priority of the ingredients to list according to the user's emotions, more appropriate ingredients can be listed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the listing unit may be performed using AI or not using AI.

[0094] The listing unit can improve the accuracy of the listing by considering the interrelationships of ingredients during the listing process. For example, the listing unit can group together ingredients used in the same dish. The listing unit can also list ingredients considering their shelf life. The listing unit can also list ingredients based on their cooking method. This allows for the listing of more appropriate ingredients by considering the interrelationships of ingredients. Some or all of the above processing in the listing unit may be performed using AI, or it may be performed without using AI.

[0095] The listing unit can perform listing while considering the attribute information of the food supplier. For example, the listing unit can prioritize listing organic food. The listing unit can also prioritize listing local food. The listing unit can also prioritize listing food from a specific brand. This allows for the listing of more appropriate food by considering the attribute information of the food supplier. Some or all of the above processing in the listing unit may be performed using AI or not.

[0096] The listing unit can estimate the user's emotions and adjust how the listed ingredients are displayed based on the estimated emotions. For example, if the user is relaxed, the listing unit can display detailed information. If the user is in a hurry, the listing unit can also display concise information. If the user is excited, the listing unit can also provide a visually appealing display. This allows for the provision of more appropriate information by adjusting how ingredients are displayed 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using AI or not.

[0097] The listing unit can consider the geographical distribution of ingredients when listing them. For example, the listing unit can prioritize listing local ingredients. The listing unit can also list specialty products from specific regions. The listing unit can also avoid listing imported ingredients. This allows for the listing of more appropriate ingredients by considering the geographical distribution of ingredients. Some or all of the above processing in the listing unit may be performed using AI or not.

[0098] The listing unit can improve the accuracy of its listings by referring to relevant literature on ingredients during the listing process. For example, the listing unit can list ingredients by referring to their nutritional information. The listing unit can also list ingredients by referring to their cooking methods. The listing unit can also list ingredients by referring to their storage methods. This allows for the listing of more appropriate ingredients by referring to relevant literature on ingredients. Some or all of the above processing in the listing unit may be performed using AI, or it may be performed without using AI.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can estimate the user's emotions and adjust the timing of order acceptance based on the estimated emotions. For example, if the user is stressed, the order can be accepted quickly. If the user is relaxed, the order can be accepted after reviewing the detailed order contents. If the user is in a hurry, a simplified order acceptance procedure can be provided. This allows orders to be accepted at a more appropriate time by adjusting the timing of order acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0101] The reception desk can analyze a user's past order history and select the optimal reception method. For example, it can prioritize suggesting menus that the user has frequently ordered in the past. It can also suggest menus suitable for specific time slots based on the user's past order history. It can also analyze and suggest preferences based on the user's past order history. In this way, the optimal reception method can be selected by analyzing the user's past order history. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0102] The reception desk can filter orders based on the user's current dietary preferences and health status. For example, if a user is health-conscious, it can prioritize suggesting nutritionally balanced menus. If a user wants to avoid a particular ingredient, it can suggest menus that do not contain that ingredient. If a user is on a specific diet, it can suggest menus suitable for that diet. In this way, by filtering based on the user's current dietary preferences and health status, more appropriate menus can be suggested. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0103] The reception desk can estimate the user's emotions and determine the priority of orders based on the estimated emotions. For example, if the user is in a hurry, the order can be processed with the highest priority. If the user is relaxed, the order can be processed while balancing it with other orders. If the user is stressed, the order can be processed quickly. This allows orders to be processed in a more appropriate order by determining the priority of orders 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0104] The reception desk can prioritize orders based on the user's geographical location when receiving an order. For example, if a user is in a specific region, it can prioritize suggesting ingredients available in that region. If a user is traveling, it can also suggest ingredients available at their travel destination. If a user is at home, it can also suggest ingredients available around their home. This allows for prioritizing orders based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0105] The generation unit can estimate the user's emotions and adjust the presentation of the menu plan based on the estimated emotions. For example, if the user is relaxed, it can provide a menu plan with detailed explanations. If the user is in a hurry, it can provide a concise menu plan. If the user is excited, it can provide a visually appealing menu plan. In this way, by adjusting the presentation of the menu plan according to the user's emotions, a more appropriate menu plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generative AI.

[0106] The generation unit can adjust the level of detail in the menu based on the importance of the ingredients when generating a menu plan. For example, it can provide detailed cooking methods for major ingredients, and brief descriptions for auxiliary ingredients. If a particular ingredient is important, the menu can be built around that ingredient. By adjusting the level of detail in the menu based on the importance of the ingredients, a more appropriate menu plan can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0107] The generation unit can apply different generation algorithms depending on the category of cuisine when generating menu plans. For example, in the case of Chinese cuisine, menus can be generated based on specific seasonings and cooking methods. In the case of Japanese cuisine, menus can be generated based on traditional cooking methods. In the case of Western cuisine, menus can be generated based on modern cooking methods. By applying different generation algorithms depending on the category of cuisine, a wider variety of menu plans can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0108] The generation unit can estimate the user's emotions and adjust the length of the meal plan based on the estimated emotions. For example, if the user is in a hurry, a short meal plan can be provided. If the user is relaxed, a longer meal plan can be provided. If the user is excited, a varied meal plan can be provided. In this way, by adjusting the length of the meal plan according to the user's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generative AI.

[0109] The generation unit can determine the priority of menus based on the availability of ingredients when generating menu plans. For example, it can propose menus that prioritize the use of seasonal ingredients. It can also propose menus that avoid ingredients that are difficult to obtain. It can also propose menus that use ingredients that are only available at specific times of the year. By determining the priority of menus based on the availability of ingredients, it is possible to provide more appropriate menu plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives the user's order. The user's order includes the type of meal, ingredient specifications, allergy information, etc. For example, it accepts an order from a user saying, "Please prepare a one-month menu for breakfast, lunch, and dinner. Mostly Chinese food, with less seafood. Please buy the seasonings online. I'll buy the fresh food at the supermarket, so please make a list of what I need." Step 2: The generation unit uses a generation AI to create a menu plan based on the information received by the reception unit. The generation unit collects and analyzes recipe information from the internet to create a menu plan tailored to the user's preferences. The generation unit can also recreate the menu plan in response to user revision requests. For example, if a user requests a revision such as, "I'll just eat whatever for lunch, so buy frozen fried rice twice a week and instant noodles of various flavors about twice a week online. Also, make a list of easy-to-eat items, and then decide what to buy from that list," the generation AI will create a new menu plan. Step 3: The purchasing unit automatically purchases ingredients available online based on the menu plan created by the generation unit. The purchasing unit automatically purchases ingredients available online from the generation AI. The generation AI collects product information from shopping sites and automatically purchases ingredients according to the user's preferences. Step 4: The listing unit lists the fresh food items that need to be purchased at the supermarket based on the menu plan created by the generation unit. The listing unit uses the generation AI to list the fresh food items that need to be purchased at the supermarket. The generation AI lists fresh food items according to the user's preferences and provides them to the user.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, purchase unit, and listing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's order. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a menu plan using generation AI. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically purchases ingredients that can be purchased online. The listing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and lists fresh food items that need to be purchased at the supermarket. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, purchase unit, and listing unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's order. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a menu plan using generation AI. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically purchases ingredients that can be purchased online. The listing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and lists fresh food items that need to be purchased at the supermarket. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, purchase unit, and listing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's order. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a menu plan using generation AI. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically purchases ingredients that can be purchased online. The listing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and lists fresh food items that need to be purchased at the supermarket. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, purchase unit, and listing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user orders. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates menu plans using generation AI. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically purchases ingredients that can be purchased online. The listing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and lists fresh food items that need to be purchased at the supermarket. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The reception area that takes user orders, A generation unit that creates a menu plan based on the information received by the reception unit, A purchasing unit automatically purchases ingredients that can be purchased online based on the menu plan created by the generation unit, The system includes a listing unit that lists fresh food items that need to be purchased at a supermarket based on the menu plan created by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates menu plans tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The system uses AI to collect and analyze recipe information from the internet and create meal plans tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned purchasing department, The AI ​​generates a list of ingredients that can be purchased online and automatically buys them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned listing unit is, The AI ​​generates a list of fresh food items that need to be purchased at the supermarket. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The AI ​​generates menu plans and recreates them based on user requests for modifications. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of order acceptance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past order history and select the optimal order processing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When taking an order, filtering is performed based on the user's current dietary preferences and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of orders to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting an order, the system prioritizes orders that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking an order, the system analyzes the user's social media activity and accepts relevant orders. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts how the menu plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a menu plan, adjust the level of detail in the menu based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating menu plans, different generation algorithms are applied depending on the category of the dishes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the length of the menu plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a menu plan, the priority of the menu items is determined based on when the ingredients are available. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a menu plan, the order of the dishes is adjusted based on the relationships between ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned purchasing department, It estimates the user's emotions and determines the priority of ingredients to purchase based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned purchasing department, When purchasing ingredients, consider their interrelationships to improve the accuracy of your purchase. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned purchasing department, When purchasing ingredients, consider the attribute information of the supplier. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned purchasing department, The system estimates the user's emotions and adjusts how the ingredients are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned purchasing department, When purchasing ingredients, consider their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, When purchasing ingredients, refer to relevant literature to improve the accuracy of your purchase. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned listing unit is, The system estimates the user's emotions and determines the priority of the ingredients to list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned listing unit is, When creating a list, consider the interrelationships between ingredients to improve the accuracy of the list. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned listing unit is, When creating the list, the attribute information of the food supplier is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned listing unit is, The system estimates the user's emotions and adjusts how the listed ingredients are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned listing unit is, When creating the list, take into account the geographical distribution of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned listing unit is, When creating a list, refer to relevant literature on ingredients to improve the accuracy of the list. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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. The reception area that takes user orders, A generation unit that creates a menu plan based on the information received by the reception unit, A purchasing unit automatically purchases ingredients that can be purchased online based on the menu plan created by the generation unit, The system includes a listing unit that lists fresh food items that need to be purchased at a supermarket based on the menu plan created by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates menu plans tailored to the user's preferences. The system according to feature 1.

3. The generating unit is The system uses AI to collect and analyze recipe information from the internet, creating meal plans tailored to the user's preferences. The system according to feature 1.

4. The aforementioned purchasing department, The AI ​​generates and automatically purchases groceries that can be bought online. The system according to feature 1.

5. The aforementioned listing unit is, The AI ​​generates a list of fresh food items that need to be purchased at the supermarket. The system according to feature 1.

6. The generating unit is The AI ​​generates menu plans and recreates them based on user requests for modifications. The system according to feature 1.

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

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

9. The aforementioned reception unit is When taking an order, filtering is performed based on the user's current dietary preferences and health status. The system according to feature 1.

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