system

Abstract

The system according to this embodiment aims to generate an optimal menu based on budget and conditions, and to order the ingredients. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an ordering unit. The reception unit receives input conditions. The analysis unit analyzes the conditions entered by the reception unit. The generation unit generates a menu based on the conditions analyzed by the analysis unit. The ordering unit orders ingredients based on the menu generated 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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating an optimal menu based on a budget and conditions and ordering ingredients is time-consuming.

[0005] The system according to the embodiment aims to generate an optimal menu based on a budget and conditions and order ingredients.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and an order unit. The reception unit inputs conditions. The analysis unit analyzes the conditions input by the reception unit. The generation unit generates a menu based on the conditions analyzed by the analysis unit. The order unit orders ingredients based on the menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate an optimal menu based on budget and conditions, and order ingredients. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 suggestion system according to an embodiment of the present invention is a system in which an AI app suggests a one-week menu based on conditions such as budget, family size, nutritional requirements, and dietary restrictions due to illness. If there are no problems with the menu, it is automatically linked to an e-commerce site and ingredients are delivered. The menu suggestion system can reduce the time spent on daily meal planning and shopping for people of all ages. For example, a user inputs conditions such as budget, family size, nutritional requirements, and dietary restrictions due to illness via a messaging app. For example, if the budget is 5,000 yen, the family size is 4 people, the nutritional requirement is high protein and low fat, and the dietary restriction due to illness is diabetes, these conditions are entered. This information is input into the AI ​​app. Next, the AI ​​app analyzes the entered conditions and suggests a one-week menu. The AI ​​app generates the optimal menu based on the conditions. For example, it suggests a menu for one week, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. If there are no problems with the suggested menu, the AI ​​app automatically links to an e-commerce site and orders the necessary ingredients. For example, based on a suggested menu, users can order ingredients such as oatmeal, chicken salad, and grilled chicken. This order is sent to an e-commerce site, and the ingredients are delivered. This system allows users to handle a week's worth of meal planning and shopping in one go, with only one action required per week. This significantly reduces the time spent on daily meal planning and shopping. For instance, a user can input their requirements once a week via a messaging app and order ingredients based on the menu suggested by the AI ​​app, saving them the trouble of daily meal planning and shopping. Furthermore, because the AI ​​app suggests the optimal menu based on the user's requirements, it can provide nutritionally balanced meals. This service is extremely convenient and reduces the workload for people of all ages who spend time on daily meal planning and shopping. Also, because it only requires one action per week, it is very useful for busy modern people. In this way, the meal planning system can suggest the optimal menu based on the user's requirements and automate the ordering and delivery of ingredients.

[0029] The menu suggestion system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an ordering unit. The reception unit allows the user to input conditions such as budget, family structure, nutritional requirements, and dietary restrictions due to illness. For example, the reception unit provides an interface for the user to input conditions via a messaging app. The analysis unit analyzes the conditions entered by the reception unit. For example, the analysis unit uses AI to analyze the entered conditions and extracts data for generating an optimal menu. The generation unit generates a menu based on the conditions analyzed by the analysis unit. For example, the generation unit uses AI to generate a menu for one week. The generation unit suggests a menu for one week, for example, oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The ordering unit orders ingredients based on the menu generated by the generation unit. For example, the ordering unit links to an e-commerce site and automatically orders the necessary ingredients. As a result, the menu suggestion system according to this embodiment can suggest an optimal menu based on the user's conditions and automate the ordering and delivery of ingredients.

[0030] The reception desk allows users to input conditions such as budget, family size, nutritional needs, and dietary restrictions due to illness. Specifically, it provides an interface for users to input conditions via a messaging app. For example, a user can open the app and use fields for entering a budget, a dropdown menu for selecting family size, checkboxes for entering nutritional preferences, and a text box for entering dietary restrictions due to illness. Furthermore, the reception desk has a function that remembers conditions entered by the user in the past and automatically suggests them the next time the user enters information. This saves the user the trouble of entering the same information every time. The reception desk also has a voice input function, allowing users to input conditions by voice. For example, if a user gives a voice command such as "Budget is 5000 yen per week, family size is 4 people, nutritional needs are high protein and low fat, and carbohydrate restriction is required due to diabetes," the system will convert this into text and accept it as conditions. In this way, the reception desk can meet the diverse needs of users and provide a user-friendly interface.

[0031] The analysis unit analyzes the conditions entered by the reception unit. Specifically, it uses AI to analyze the entered conditions and extract data to generate the optimal menu. For example, the AI ​​uses natural language processing technology to analyze the text data entered by the user and accurately extracts various conditions such as budget, family structure, nutritional aspects, and dietary restrictions due to illness. Furthermore, the AI ​​can utilize past data and statistical information to take into account the user's preferences and past selection history. For example, based on data of menus and ingredients previously selected by the user, it can identify ingredients that the user likes and ingredients that they want to avoid. The analysis unit also connects with a database containing expertise in nutrition, allowing it to refer to nutritional value, calories, and allergen information for each ingredient. This enables the analysis unit to select the ingredients and dishes best suited to the user's conditions and provide the basic data for generating the optimal menu. In addition, the analysis unit can take into account real-time updated market prices and seasonal ingredient information to propose the most cost-effective menu within the budget. As a result, the analysis unit can respond to diverse user conditions and provide data for generating the optimal menu quickly and accurately.

[0032] The generation unit generates menus based on the conditions analyzed by the analysis unit. Specifically, it uses AI to generate a week's worth of menus. For example, the generation unit might suggest a week's worth of menus, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The AI ​​uses an algorithm that optimizes nutritional balance, calories, and ingredient combinations based on the user's conditions. For example, if a user desires a high-protein, low-fat diet, the generation unit will create a menu centered around high-protein ingredients such as chicken breast, fish, and legumes. It will also suggest low-carbohydrate ingredients and dishes for users with diabetes. Furthermore, the generation unit can take into account the user's preferences and past selection history to prioritize suggesting dishes and ingredients the user likes. For example, it can identify dishes the user likes based on data from menus and ingredients the user has previously selected and incorporate them into the menu. The generation unit can also utilize seasonal and in-season ingredients to suggest menus that are highly nutritious and cost-effective. As a result, the generation unit can generate menus that are best suited to the user's conditions and provide healthy and balanced meals.

[0033] The ordering unit orders ingredients based on the menus generated by the generation unit. Specifically, it integrates with e-commerce sites and automatically orders the necessary ingredients. For example, the ordering unit creates a list of necessary ingredients based on the generated menu and sends orders to partner online supermarkets and ingredient delivery services. The ordering unit can set the optimal delivery schedule, taking into account the user's address and preferred delivery time. Furthermore, the ordering unit remembers ingredients and brands that the user has purchased in the past and has a function to prioritize ordering ingredients of the same brand and quality. For example, if the user prefers a particular brand of organic vegetables, the ordering unit will prioritize ordering vegetables of that brand. The ordering unit can also monitor inventory status and price fluctuations in real time and select the most cost-effective ingredients. As a result, the ordering unit can quickly and accurately order the optimal ingredients based on the user's conditions and automate the ordering and delivery of ingredients. In addition, the ordering unit provides a service that users can use with peace of mind by notifying them of the order status and delivery status in real time. For example, by sending notifications to the user when the order is confirmed and when delivery is completed, the user can always stay up-to-date. This allows the ordering department to improve user convenience and enable smooth ordering and delivery of ingredients.

[0034] The reception desk allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. The reception desk provides an interface for users to input conditions, for example, through a messaging app. When users input a budget, the reception desk allows them to input, for example, a monthly budget or a budget per meal. When users input family structure, the reception desk allows them to input, for example, the number of family members, their age range, and specific dietary restrictions. When users input nutritional needs, the reception desk allows them to input, for example, nutrients such as calories, vitamins, and minerals. When users input dietary restrictions due to illness, the reception desk allows them to input conditions such as diabetes, allergies, and hypertension. This allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the conditions entered by the user into an AI, which can then analyze the conditions.

[0035] The analysis unit can analyze the conditions entered by the reception unit and generate the optimal menu. The analysis unit can, for example, use AI to analyze the entered conditions and extract data for generating the optimal menu. The analysis unit can, for example, use a data analysis algorithm to analyze the entered conditions. The analysis unit can, for example, refer to past analysis data to improve the accuracy of the analysis. The analysis unit can, for example, select the optimal algorithm based on past analysis data to improve the accuracy of the analysis. The analysis unit can, for example, refer to past analysis data and optimize the analysis results for specific conditions. The analysis unit can, for example, analyze past analysis data and adjust the algorithm parameters to improve the accuracy of the analysis. This enables the generation of the optimal menu based on the entered conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the conditions entered by the user into the AI, and the AI ​​can analyze the conditions.

[0036] The generation unit can generate a week's worth of meal plans. The generation unit can generate a week's worth of meal plans using, for example, AI. The generation unit can suggest a week's worth of meal plans, for example, oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The generation unit can generate meal plans that take nutritional balance into consideration. The generation unit can optimize nutritional balance by considering, for example, the user's health data and lifestyle data. The generation unit can suggest a meal plan that takes appropriate nutritional balance into consideration by referring to the user's health data. The generation unit can suggest a meal plan that reflects appropriate dietary restrictions based on the user's lifestyle data. The generation unit can suggest a meal plan that takes necessary nutrients into consideration by referring to the user's past health checkup results. This allows for the automatic generation of a week's worth of meal plans. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user-entered conditions into the AI, which can analyze the conditions and generate an optimal meal plan.

[0037] The ordering unit can order ingredients based on the menu generated by the generation unit. The ordering unit can, for example, link to an e-commerce site and automatically order the necessary ingredients. The ordering unit can, for example, order ingredients such as oatmeal, salad chicken, and grilled chicken based on the generated menu. The ordering unit can, for example, send the order to an e-commerce site and have the ingredients delivered. The ordering unit can, for example, save users the trouble of daily meal planning and shopping by allowing them to input conditions once a week via a messaging app and order ingredients based on a menu suggested by an AI app. This allows for the automatic ordering of ingredients based on the generated menu. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not using AI. For example, the ordering unit can input the menu generated by the generation unit into the AI, and the AI ​​can order the necessary ingredients.

[0038] The ordering unit can connect to e-commerce sites and order ingredients. For example, the ordering unit can connect to e-commerce sites and automatically order the necessary ingredients. For example, the ordering unit can order ingredients such as oatmeal, chicken salad, and grilled chicken based on a generated menu. For example, the ordering unit sends the order to the e-commerce site and the ingredients are delivered. For example, the ordering unit can save users the trouble of daily meal planning and shopping by allowing them to input conditions once a week via a messaging app and order ingredients based on a menu suggested by an AI app. This allows the ordering unit to connect to e-commerce sites and order ingredients. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not. For example, the ordering unit can input the menu generated by the generation unit into the AI, and the AI ​​can order the necessary ingredients.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest conditions to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception unit can automatically complete input fields based on the user's current health status and lifestyle when conditions are entered. For example, the reception unit can refer to the user's health data and automatically complete input fields that take into account an appropriate nutritional balance. For example, the reception unit can refer to the user's lifestyle data and automatically complete input fields that reflect appropriate dietary restrictions. For example, the reception unit can refer to the user's past health checkup results and automatically complete input fields that take into account necessary nutrients. This allows the system to automatically complete input fields based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data and lifestyle data into AI, which can then automatically complete the input fields.

[0041] The reception desk can suggest regionally specific ingredients and dishes while considering the user's geographical location information when conditions are entered. For example, the reception desk can suggest regionally specific ingredients based on the user's current location. For example, the reception desk can suggest dishes appropriate for the region's season by referring to the user's geographical location information. For example, the reception desk can suggest dishes using local specialties based on the user's geographical location information. In this way, regionally specific ingredients and dishes can be suggested based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the AI, and the AI ​​can suggest regionally specific ingredients and dishes.

[0042] The reception unit can analyze the user's social media activity when conditions are entered and automatically input relevant conditions. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant ingredients or dishes. For example, the reception unit can refer to the content of the user's social media followers and friends and automatically input relevant conditions. For example, the reception unit can suggest ingredients or dishes that the user has shown interest in in the past based on their social media activity history. This allows for the automatic input of relevant conditions based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can automatically input relevant conditions.

[0043] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal algorithm based on past analysis data to improve analysis accuracy. For example, the analysis unit can optimize the analysis results for specific conditions by referring to past analysis data. For example, the analysis unit can analyze past analysis data and adjust the algorithm parameters to improve analysis accuracy. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, and the AI ​​can optimize the analysis algorithm.

[0044] The analysis unit can improve the accuracy of its analysis by considering the user's health data and lifestyle data during the analysis process. For example, the analysis unit may refer to the user's health data and perform an analysis that considers an appropriate nutritional balance. For example, the analysis unit may perform an analysis that reflects appropriate dietary restrictions based on the user's lifestyle data. For example, the analysis unit may refer to the user's past health checkup results and perform an analysis that considers the necessary nutrients. This allows the analysis accuracy to be improved based on the user's health data and lifestyle data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the user's health data and lifestyle data into the AI, which can then improve the accuracy of the analysis.

[0045] The analysis unit can incorporate region-specific ingredients and dishes into its analysis by considering the user's geographical location information. For example, the analysis unit can incorporate region-specific ingredients into its analysis based on the user's current location. For example, the analysis unit can incorporate dishes appropriate for the region's season by referring to the user's geographical location information. For example, the analysis unit can incorporate dishes using local specialties into its analysis based on the user's geographical location information. This allows the analysis to incorporate region-specific ingredients and dishes based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the AI, which can then incorporate region-specific ingredients and dishes into its analysis.

[0046] The analysis unit can analyze the user's social media activity during analysis and utilize relevant data for the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect relevant ingredients and dishes in the analysis. For example, the analysis unit can refer to the content of posts by the user's social media followers and friends and utilize relevant data for the analysis. For example, the analysis unit can reflect ingredients and dishes that the user has shown interest in in the past based on the user's social media activity history. This allows relevant data to be used for analysis based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into AI, and the AI ​​can utilize the relevant data for analysis.

[0047] The generation unit can suggest the optimal menu by referring to the user's past meal history when generating a menu. For example, the generation unit can suggest the optimal menu based on dishes the user has enjoyed eating in the past. For example, the generation unit can suggest a menu that takes nutritional balance into account based on the user's past meal history. For example, the generation unit can analyze the user's past meal history and suggest a menu using specific ingredients. This makes it possible to suggest the optimal menu based on the user's past meal history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history into AI, and the AI ​​can suggest the optimal menu.

[0048] The generation unit can optimize nutritional balance when generating menus, taking into account the user's health data and lifestyle data. For example, the generation unit can refer to the user's health data and propose a menu that considers appropriate nutritional balance. For example, the generation unit can propose a menu that reflects appropriate dietary restrictions based on the user's lifestyle data. For example, the generation unit can refer to the user's past health checkup results and propose a menu that considers necessary nutrients. This allows for the optimization of nutritional balance based on the user's health data and lifestyle data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data and lifestyle data into AI, which can then optimize the nutritional balance.

[0049] The generation unit can suggest regionally specific ingredients and dishes while considering the user's geographical location information during menu generation. For example, the generation unit can suggest a menu using regionally specific ingredients based on the user's current location. For example, the generation unit can suggest dishes appropriate to the region's season by referring to the user's geographical location information. For example, the generation unit can suggest a menu using regional specialty products based on the user's geographical location information. This makes it possible to suggest regionally specific ingredients and dishes based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can suggest regionally specific ingredients and dishes.

[0050] The generation unit can analyze the user's social media activity and suggest relevant ingredients and dishes when generating menus. For example, the generation unit can analyze the content of the user's social media posts and suggest relevant ingredients and dishes. For example, the generation unit can refer to the content of posts by the user's social media followers and friends and suggest relevant ingredients and dishes. For example, the generation unit can suggest ingredients and dishes that the user has shown interest in in the past based on the user's social media activity history. In this way, it is possible to suggest relevant ingredients and dishes based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can suggest relevant ingredients and dishes.

[0051] The ordering system can suggest the optimal ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as candidates. For example, the ordering system can prioritize suggesting ordering methods (voice, text, etc.) that the user has used in the past. For example, the ordering system can predict and suggest ingredients to be used at a specific time of day based on the user's past order history. This allows the system to suggest the optimal ordering method based on the user's past order history. Some or all of the above processes in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's past order history into AI, which can then suggest the optimal ordering method.

[0052] The ordering system can optimize orders by considering the user's health data and lifestyle data at the time of ordering. For example, the ordering system can refer to the user's health data and order ingredients that consider an appropriate nutritional balance. For example, the ordering system can order ingredients that reflect appropriate dietary restrictions based on the user's lifestyle data. For example, the ordering system can refer to the user's past health checkup results and order ingredients that consider the necessary nutrients. This allows the ordering system to optimize orders based on the user's health data and lifestyle data. Some or all of the above processing in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's health data and lifestyle data into AI, which can then optimize the order.

[0053] The ordering system can prioritize ordering regionally specific ingredients and dishes by considering the user's geographical location when an order is placed. For example, the ordering system can prioritize ordering regionally specific ingredients based on the user's current location. For example, the ordering system can prioritize ordering seasonal ingredients for the region by referring to the user's geographical location. For example, the ordering system can prioritize ordering local specialty products based on the user's geographical location. This allows for the prioritization of regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the ordering system may be performed using AI, or not. For example, the ordering system can input the user's geographical location into the AI, which can then prioritize ordering regionally specific ingredients and dishes.

[0054] The ordering system can analyze a user's social media activity when an order is placed and order relevant ingredients and dishes. For example, the ordering system can analyze a user's social media posts and order relevant ingredients and dishes. For example, the ordering system can refer to posts from a user's social media followers and friends and order relevant ingredients and dishes. For example, the ordering system can order ingredients and dishes that a user has shown interest in in the past based on their social media activity history. This allows the ordering system to order relevant ingredients and dishes based on the user's social media activity. Some or all of the above processes in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input the user's social media activity into an AI, which can then order relevant ingredients and dishes.

[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 the user's past input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0057] The reception unit can automatically complete input fields based on the user's current health status and lifestyle when conditions are entered. For example, it can refer to the user's health data and automatically complete input fields that take into account an appropriate nutritional balance. Based on the user's lifestyle data, it can automatically complete input fields that reflect appropriate dietary restrictions. It can also refer to the user's past health checkup results and automatically complete input fields that take into account necessary nutrients. In this way, input fields can be automatically completed based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data and lifestyle data into AI, and the AI ​​can automatically complete the input fields.

[0058] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, it can select the optimal algorithm based on past analysis data to improve analysis accuracy. It can optimize the analysis results for specific conditions by referring to past analysis data. It can also analyze past analysis data and adjust the algorithm parameters to improve analysis accuracy. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, and the AI ​​can optimize the analysis algorithm.

[0059] The generation unit can suggest the optimal menu by referring to the user's past meal history when generating menus. For example, it can suggest the optimal menu based on dishes the user has enjoyed eating in the past. It can also suggest a menu that takes nutritional balance into consideration based on the user's past meal history. Furthermore, it can analyze the user's past meal history and suggest a menu using specific ingredients. This allows the generation unit to suggest the optimal menu based on the user's past meal history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history into AI, and the AI ​​can suggest the optimal menu.

[0060] The ordering system can suggest the optimal ordering method by referring to the user's past order history when an order is placed. For example, it can automatically display ingredients that the user has frequently ordered in the past as suggestions. It can also prioritize suggesting ordering methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest ingredients to be used at a specific time of day based on the user's past order history. This allows the system to suggest the optimal ordering method based on the user's past order history. Some or all of the above processes in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input the user's past order history into an AI, which can then suggest the optimal ordering method.

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

[0062] Step 1: The reception desk allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. For example, the reception desk provides an interface for users to input conditions via a messaging app. Step 2: The analysis unit analyzes the conditions entered by the reception unit. For example, the analysis unit uses AI to analyze the entered conditions and extract data to generate the optimal menu. Step 3: The generation unit generates a menu based on the conditions analyzed by the analysis unit. For example, the generation unit uses AI to generate a week's worth of menus. The generation unit suggests a menu for the week, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. Step 4: The ordering unit orders ingredients based on the menu generated by the generation unit. For example, the ordering unit connects to an e-commerce site and automatically orders the necessary ingredients.

[0063] (Example of form 2) The menu suggestion system according to an embodiment of the present invention is a system in which an AI app suggests a one-week menu based on conditions such as budget, family size, nutritional requirements, and dietary restrictions due to illness. If there are no problems with the menu, it is automatically linked to an e-commerce site and ingredients are delivered. The menu suggestion system can reduce the time spent on daily meal planning and shopping for people of all ages. For example, a user inputs conditions such as budget, family size, nutritional requirements, and dietary restrictions due to illness via a messaging app. For example, if the budget is 5,000 yen, the family size is 4 people, the nutritional requirement is high protein and low fat, and the dietary restriction due to illness is diabetes, these conditions are entered. This information is input into the AI ​​app. Next, the AI ​​app analyzes the entered conditions and suggests a one-week menu. The AI ​​app generates the optimal menu based on the conditions. For example, it suggests a menu for one week, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. If there are no problems with the suggested menu, the AI ​​app automatically links to an e-commerce site and orders the necessary ingredients. For example, based on a suggested menu, users can order ingredients such as oatmeal, chicken salad, and grilled chicken. This order is sent to an e-commerce site, and the ingredients are delivered. This system allows users to handle a week's worth of meal planning and shopping in one go, with only one action required per week. This significantly reduces the time spent on daily meal planning and shopping. For instance, a user can input their requirements once a week via a messaging app and order ingredients based on the menu suggested by the AI ​​app, saving them the trouble of daily meal planning and shopping. Furthermore, because the AI ​​app suggests the optimal menu based on the user's requirements, it can provide nutritionally balanced meals. This service is extremely convenient and reduces the workload for people of all ages who spend time on daily meal planning and shopping. Also, because it only requires one action per week, it is very useful for busy modern people. In this way, the meal planning system can suggest the optimal menu based on the user's requirements and automate the ordering and delivery of ingredients.

[0064] The menu suggestion system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an ordering unit. The reception unit allows the user to input conditions such as budget, family structure, nutritional requirements, and dietary restrictions due to illness. For example, the reception unit provides an interface for the user to input conditions via a messaging app. The analysis unit analyzes the conditions entered by the reception unit. For example, the analysis unit uses AI to analyze the entered conditions and extracts data for generating an optimal menu. The generation unit generates a menu based on the conditions analyzed by the analysis unit. For example, the generation unit uses AI to generate a menu for one week. The generation unit suggests a menu for one week, for example, oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The ordering unit orders ingredients based on the menu generated by the generation unit. For example, the ordering unit links to an e-commerce site and automatically orders the necessary ingredients. As a result, the menu suggestion system according to this embodiment can suggest an optimal menu based on the user's conditions and automate the ordering and delivery of ingredients.

[0065] The reception desk allows users to input conditions such as budget, family size, nutritional needs, and dietary restrictions due to illness. Specifically, it provides an interface for users to input conditions via a messaging app. For example, a user can open the app and use fields for entering a budget, a dropdown menu for selecting family size, checkboxes for entering nutritional preferences, and a text box for entering dietary restrictions due to illness. Furthermore, the reception desk has a function that remembers conditions entered by the user in the past and automatically suggests them the next time the user enters information. This saves the user the trouble of entering the same information every time. The reception desk also has a voice input function, allowing users to input conditions by voice. For example, if a user gives a voice command such as "Budget is 5000 yen per week, family size is 4 people, nutritional needs are high protein and low fat, and carbohydrate restriction is required due to diabetes," the system will convert this into text and accept it as conditions. In this way, the reception desk can meet the diverse needs of users and provide a user-friendly interface.

[0066] The analysis unit analyzes the conditions entered by the reception unit. Specifically, it uses AI to analyze the entered conditions and extract data to generate the optimal menu. For example, the AI ​​uses natural language processing technology to analyze the text data entered by the user and accurately extracts various conditions such as budget, family structure, nutritional aspects, and dietary restrictions due to illness. Furthermore, the AI ​​can utilize past data and statistical information to take into account the user's preferences and past selection history. For example, based on data of menus and ingredients previously selected by the user, it can identify ingredients that the user likes and ingredients that they want to avoid. The analysis unit also connects with a database containing expertise in nutrition, allowing it to refer to nutritional value, calories, and allergen information for each ingredient. This enables the analysis unit to select the ingredients and dishes best suited to the user's conditions and provide the basic data for generating the optimal menu. In addition, the analysis unit can take into account real-time updated market prices and seasonal ingredient information to propose the most cost-effective menu within the budget. As a result, the analysis unit can respond to diverse user conditions and provide data for generating the optimal menu quickly and accurately.

[0067] The generation unit generates menus based on the conditions analyzed by the analysis unit. Specifically, it uses AI to generate a week's worth of menus. For example, the generation unit might suggest a week's worth of menus, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The AI ​​uses an algorithm that optimizes nutritional balance, calories, and ingredient combinations based on the user's conditions. For example, if a user desires a high-protein, low-fat diet, the generation unit will create a menu centered around high-protein ingredients such as chicken breast, fish, and legumes. It will also suggest low-carbohydrate ingredients and dishes for users with diabetes. Furthermore, the generation unit can take into account the user's preferences and past selection history to prioritize suggesting dishes and ingredients the user likes. For example, it can identify dishes the user likes based on data from menus and ingredients the user has previously selected and incorporate them into the menu. The generation unit can also utilize seasonal and in-season ingredients to suggest menus that are highly nutritious and cost-effective. As a result, the generation unit can generate menus that are best suited to the user's conditions and provide healthy and balanced meals.

[0068] The ordering unit orders ingredients based on the menus generated by the generation unit. Specifically, it integrates with e-commerce sites and automatically orders the necessary ingredients. For example, the ordering unit creates a list of necessary ingredients based on the generated menu and sends orders to partner online supermarkets and ingredient delivery services. The ordering unit can set the optimal delivery schedule, taking into account the user's address and preferred delivery time. Furthermore, the ordering unit remembers ingredients and brands that the user has purchased in the past and has a function to prioritize ordering ingredients of the same brand and quality. For example, if the user prefers a particular brand of organic vegetables, the ordering unit will prioritize ordering vegetables of that brand. The ordering unit can also monitor inventory status and price fluctuations in real time and select the most cost-effective ingredients. As a result, the ordering unit can quickly and accurately order the optimal ingredients based on the user's conditions and automate the ordering and delivery of ingredients. In addition, the ordering unit provides a service that users can use with peace of mind by notifying them of the order status and delivery status in real time. For example, by sending notifications to the user when the order is confirmed and when delivery is completed, the user can always stay up-to-date. This allows the ordering department to improve user convenience and enable smooth ordering and delivery of ingredients.

[0069] The reception desk allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. The reception desk provides an interface for users to input conditions, for example, through a messaging app. When users input a budget, the reception desk allows them to input, for example, a monthly budget or a budget per meal. When users input family structure, the reception desk allows them to input, for example, the number of family members, their age range, and specific dietary restrictions. When users input nutritional needs, the reception desk allows them to input, for example, nutrients such as calories, vitamins, and minerals. When users input dietary restrictions due to illness, the reception desk allows them to input conditions such as diabetes, allergies, and hypertension. This allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the conditions entered by the user into an AI, which can then analyze the conditions.

[0070] The analysis unit can analyze the conditions entered by the reception unit and generate the optimal menu. The analysis unit can, for example, use AI to analyze the entered conditions and extract data for generating the optimal menu. The analysis unit can, for example, use a data analysis algorithm to analyze the entered conditions. The analysis unit can, for example, refer to past analysis data to improve the accuracy of the analysis. The analysis unit can, for example, select the optimal algorithm based on past analysis data to improve the accuracy of the analysis. The analysis unit can, for example, refer to past analysis data and optimize the analysis results for specific conditions. The analysis unit can, for example, analyze past analysis data and adjust the algorithm parameters to improve the accuracy of the analysis. This enables the generation of the optimal menu based on the entered conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the conditions entered by the user into the AI, and the AI ​​can analyze the conditions.

[0071] The generation unit can generate a week's worth of meal plans. The generation unit can generate a week's worth of meal plans using, for example, AI. The generation unit can suggest a week's worth of meal plans, for example, oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. The generation unit can generate meal plans that take nutritional balance into consideration. The generation unit can optimize nutritional balance by considering, for example, the user's health data and lifestyle data. The generation unit can suggest a meal plan that takes appropriate nutritional balance into consideration by referring to the user's health data. The generation unit can suggest a meal plan that reflects appropriate dietary restrictions based on the user's lifestyle data. The generation unit can suggest a meal plan that takes necessary nutrients into consideration by referring to the user's past health checkup results. This allows for the automatic generation of a week's worth of meal plans. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user-entered conditions into the AI, which can analyze the conditions and generate an optimal meal plan.

[0072] The ordering unit can order ingredients based on the menu generated by the generation unit. The ordering unit can, for example, link to an e-commerce site and automatically order the necessary ingredients. The ordering unit can, for example, order ingredients such as oatmeal, salad chicken, and grilled chicken based on the generated menu. The ordering unit can, for example, send the order to an e-commerce site and have the ingredients delivered. The ordering unit can, for example, save users the trouble of daily meal planning and shopping by allowing them to input conditions once a week via a messaging app and order ingredients based on a menu suggested by an AI app. This allows for the automatic ordering of ingredients based on the generated menu. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not using AI. For example, the ordering unit can input the menu generated by the generation unit into the AI, and the AI ​​can order the necessary ingredients.

[0073] The ordering unit can connect to e-commerce sites and order ingredients. For example, the ordering unit can connect to e-commerce sites and automatically order the necessary ingredients. For example, the ordering unit can order ingredients such as oatmeal, chicken salad, and grilled chicken based on a generated menu. For example, the ordering unit sends the order to the e-commerce site and the ingredients are delivered. For example, the ordering unit can save users the trouble of daily meal planning and shopping by allowing them to input conditions once a week via a messaging app and order ingredients based on a menu suggested by an AI app. This allows the ordering unit to connect to e-commerce sites and order ingredients. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not. For example, the ordering unit can input the menu generated by the generation unit into the AI, and the AI ​​can order the necessary ingredients.

[0074] The reception unit can estimate the user's emotions and customize the condition input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception unit can prioritize voice input to allow for quick condition input. This allows the condition input interface to be customized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into an AI, which can estimate the emotions and customize the interface.

[0075] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest conditions to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0076] The reception unit can automatically complete input fields based on the user's current health status and lifestyle when conditions are entered. For example, the reception unit can refer to the user's health data and automatically complete input fields that take into account an appropriate nutritional balance. For example, the reception unit can refer to the user's lifestyle data and automatically complete input fields that reflect appropriate dietary restrictions. For example, the reception unit can refer to the user's past health checkup results and automatically complete input fields that take into account necessary nutrients. This allows the system to automatically complete input fields based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data and lifestyle data into AI, which can then automatically complete the input fields.

[0077] The reception desk can estimate the user's emotions and adjust the priority of input fields based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying important fields and simplify the input process. If the user is relaxed, for example, the reception desk may provide detailed input fields and suggest customizable input methods. If the user is in a hurry, for example, the reception desk may display the most important fields first to allow for quick input. This allows the priority of input fields to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into an AI, which can estimate the emotions and adjust the priority of input fields.

[0078] The reception desk can suggest regionally specific ingredients and dishes while considering the user's geographical location information when conditions are entered. For example, the reception desk can suggest regionally specific ingredients based on the user's current location. For example, the reception desk can suggest dishes appropriate for the region's season by referring to the user's geographical location information. For example, the reception desk can suggest dishes using local specialties based on the user's geographical location information. In this way, regionally specific ingredients and dishes can be suggested based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the AI, and the AI ​​can suggest regionally specific ingredients and dishes.

[0079] The reception unit can analyze the user's social media activity when conditions are entered and automatically input relevant conditions. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant ingredients or dishes. For example, the reception unit can refer to the content of the user's social media followers and friends and automatically input relevant conditions. For example, the reception unit can suggest ingredients or dishes that the user has shown interest in in the past based on their social media activity history. This allows for the automatic input of relevant conditions based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can automatically input relevant conditions.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the analysis results can be adjusted.

[0081] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal algorithm based on past analysis data to improve analysis accuracy. For example, the analysis unit can optimize the analysis results for specific conditions by referring to past analysis data. For example, the analysis unit can analyze past analysis data and adjust the algorithm parameters to improve analysis accuracy. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, and the AI ​​can optimize the analysis algorithm.

[0082] The analysis unit can improve the accuracy of its analysis by considering the user's health data and lifestyle data during the analysis process. For example, the analysis unit may refer to the user's health data and perform an analysis that considers an appropriate nutritional balance. For example, the analysis unit may perform an analysis that reflects appropriate dietary restrictions based on the user's lifestyle data. For example, the analysis unit may refer to the user's past health checkup results and perform an analysis that considers the necessary nutrients. This allows the analysis accuracy to be improved based on the user's health data and lifestyle data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the user's health data and lifestyle data into the AI, which can then improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will provide detailed analysis results. For example, if the user is in a hurry, the analysis unit will display the most important analysis results first. This allows the system to prioritize analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into an AI, which can estimate emotions and determine the priority of analysis results.

[0084] The analysis unit can incorporate region-specific ingredients and dishes into its analysis by considering the user's geographical location information. For example, the analysis unit can incorporate region-specific ingredients into its analysis based on the user's current location. For example, the analysis unit can incorporate dishes appropriate for the region's season by referring to the user's geographical location information. For example, the analysis unit can incorporate dishes using local specialties into its analysis based on the user's geographical location information. This allows the analysis to incorporate region-specific ingredients and dishes based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the AI, which can then incorporate region-specific ingredients and dishes into its analysis.

[0085] The analysis unit can analyze the user's social media activity during analysis and utilize relevant data for the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect relevant ingredients and dishes in the analysis. For example, the analysis unit can refer to the content of posts by the user's social media followers and friends and utilize relevant data for the analysis. For example, the analysis unit can reflect ingredients and dishes that the user has shown interest in in the past based on the user's social media activity history. This allows relevant data to be used for analysis based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into AI, and the AI ​​can utilize the relevant data for analysis.

[0086] The generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit provides a menu with detailed descriptions. If the user is in a hurry, the generation unit provides a concise and to-the-point menu. If the user is excited, the generation unit provides a visually appealing menu. This allows the presentation of the menu to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can estimate the emotions and adjust the presentation of the menu.

[0087] The generation unit can suggest the optimal menu by referring to the user's past meal history when generating a menu. For example, the generation unit can suggest the optimal menu based on dishes the user has enjoyed eating in the past. For example, the generation unit can suggest a menu that takes nutritional balance into account based on the user's past meal history. For example, the generation unit can analyze the user's past meal history and suggest a menu using specific ingredients. This makes it possible to suggest the optimal menu based on the user's past meal history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history into AI, and the AI ​​can suggest the optimal menu.

[0088] The generation unit can optimize nutritional balance when generating menus, taking into account the user's health data and lifestyle data. For example, the generation unit can refer to the user's health data and propose a menu that considers appropriate nutritional balance. For example, the generation unit can propose a menu that reflects appropriate dietary restrictions based on the user's lifestyle data. For example, the generation unit can refer to the user's past health checkup results and propose a menu that considers necessary nutrients. This allows for the optimization of nutritional balance based on the user's health data and lifestyle data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data and lifestyle data into AI, which can then optimize the nutritional balance.

[0089] The generation unit can estimate the user's emotions and determine the priority of menus based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize menus that are easy to prepare. For example, if the user is relaxed, the generation unit will suggest menus that take time to prepare. For example, if the user is in a hurry, the generation unit will prioritize menus that can be prepared in a short time. This allows the system to determine the priority of menus according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of menus.

[0090] The generation unit can suggest regionally specific ingredients and dishes while considering the user's geographical location information during menu generation. For example, the generation unit can suggest a menu using regionally specific ingredients based on the user's current location. For example, the generation unit can suggest dishes appropriate to the region's season by referring to the user's geographical location information. For example, the generation unit can suggest a menu using regional specialty products based on the user's geographical location information. This makes it possible to suggest regionally specific ingredients and dishes based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can suggest regionally specific ingredients and dishes.

[0091] The generation unit can analyze the user's social media activity and suggest relevant ingredients and dishes when generating menus. For example, the generation unit can analyze the content of the user's social media posts and suggest relevant ingredients and dishes. For example, the generation unit can refer to the content of posts by the user's social media followers and friends and suggest relevant ingredients and dishes. For example, the generation unit can suggest ingredients and dishes that the user has shown interest in in the past based on the user's social media activity history. In this way, it is possible to suggest relevant ingredients and dishes based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can suggest relevant ingredients and dishes.

[0092] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, the ordering system might prioritize ordering essential ingredients. If the user is relaxed, the ordering system might offer more detailed ordering options. If the user is in a hurry, the ordering system might allow for quick order completion. This allows for prioritizing orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 ordering system may be performed using AI or not. For example, the ordering system can input user emotion data into an AI, which can estimate the emotions and determine order priorities.

[0093] The ordering system can suggest the optimal ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as candidates. For example, the ordering system can prioritize suggesting ordering methods (voice, text, etc.) that the user has used in the past. For example, the ordering system can predict and suggest ingredients to be used at a specific time of day based on the user's past order history. This allows the system to suggest the optimal ordering method based on the user's past order history. Some or all of the above processes in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's past order history into AI, which can then suggest the optimal ordering method.

[0094] The ordering system can optimize orders by considering the user's health data and lifestyle data at the time of ordering. For example, the ordering system can refer to the user's health data and order ingredients that consider an appropriate nutritional balance. For example, the ordering system can order ingredients that reflect appropriate dietary restrictions based on the user's lifestyle data. For example, the ordering system can refer to the user's past health checkup results and order ingredients that consider the necessary nutrients. This allows the ordering system to optimize orders based on the user's health data and lifestyle data. Some or all of the above processing in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's health data and lifestyle data into AI, which can then optimize the order.

[0095] The order processing unit can estimate the user's emotions and adjust how the order is displayed based on the estimated emotions. For example, if the user is nervous, the order processing unit provides a simple and highly visible display. For example, if the user is relaxed, the order processing unit provides a display that includes detailed information. For example, if the user is in a hurry, the order processing unit provides a display that gets straight to the point. This allows the order to be 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order processing unit may be performed using AI or not using AI. For example, the order processing unit can input user emotion data into an AI, which can estimate the emotions and adjust how the order is displayed.

[0096] The ordering system can prioritize ordering regionally specific ingredients and dishes by considering the user's geographical location when an order is placed. For example, the ordering system can prioritize ordering regionally specific ingredients based on the user's current location. For example, the ordering system can prioritize ordering seasonal ingredients for the region by referring to the user's geographical location. For example, the ordering system can prioritize ordering local specialty products based on the user's geographical location. This allows for the prioritization of regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the ordering system may be performed using AI, or not. For example, the ordering system can input the user's geographical location into the AI, which can then prioritize ordering regionally specific ingredients and dishes.

[0097] The ordering system can analyze a user's social media activity when an order is placed and order relevant ingredients and dishes. For example, the ordering system can analyze a user's social media posts and order relevant ingredients and dishes. For example, the ordering system can refer to posts from a user's social media followers and friends and order relevant ingredients and dishes. For example, the ordering system can order ingredients and dishes that a user has shown interest in in the past based on their social media activity history. This allows the ordering system to order relevant ingredients and dishes based on the user's social media activity. Some or all of the above processes in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input the user's social media activity into an AI, which can then order relevant ingredients and dishes.

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

[0099] The reception desk can estimate the user's emotions and customize the condition input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. If the user is in a hurry, voice input can be prioritized to allow for quick condition input. This allows the condition input interface to be customized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can estimate the emotions and customize the interface.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the display method of the analysis results can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the analysis results can be adjusted.

[0101] The generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated emotions. For example, if the user is relaxed, it can provide a menu with detailed explanations. If the user is in a hurry, it can provide a concise and to-the-point menu. If the user is excited, it can provide a visually appealing menu. This allows the menu to be presented in accordance with 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can estimate the emotions and adjust the way the menu is presented.

[0102] The generation unit can estimate the user's emotions and determine the priority of menus based on the estimated emotions. For example, if the user is stressed, it can prioritize suggesting menus that are easy to prepare. If the user is relaxed, it can suggest menus that take time to prepare. If the user is in a hurry, it can prioritize suggesting menus that can be prepared in a short time. In this way, the priority of menus can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of menus.

[0103] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, important ingredients can be prioritized. If the user is relaxed, detailed ordering options can be provided. If the user is in a hurry, the system can allow for quick order completion. This enables the prioritization of orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 ordering system may be performed using AI or not. For example, the ordering system can input user emotion data into an AI, which can estimate the emotions and determine order priorities.

[0104] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0105] The reception unit can automatically complete input fields based on the user's current health status and lifestyle when conditions are entered. For example, it can refer to the user's health data and automatically complete input fields that take into account an appropriate nutritional balance. Based on the user's lifestyle data, it can automatically complete input fields that reflect appropriate dietary restrictions. It can also refer to the user's past health checkup results and automatically complete input fields that take into account necessary nutrients. In this way, input fields can be automatically completed based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data and lifestyle data into AI, and the AI ​​can automatically complete the input fields.

[0106] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, it can select the optimal algorithm based on past analysis data to improve analysis accuracy. It can optimize the analysis results for specific conditions by referring to past analysis data. It can also analyze past analysis data and adjust the algorithm parameters to improve analysis accuracy. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, and the AI ​​can optimize the analysis algorithm.

[0107] The generation unit can suggest the optimal menu by referring to the user's past meal history when generating menus. For example, it can suggest the optimal menu based on dishes the user has enjoyed eating in the past. It can also suggest a menu that takes nutritional balance into consideration based on the user's past meal history. Furthermore, it can analyze the user's past meal history and suggest a menu using specific ingredients. This allows the generation unit to suggest the optimal menu based on the user's past meal history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history into AI, and the AI ​​can suggest the optimal menu.

[0108] The ordering system can suggest the optimal ordering method by referring to the user's past order history when an order is placed. For example, it can automatically display ingredients that the user has frequently ordered in the past as suggestions. It can also prioritize suggesting ordering methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest ingredients to be used at a specific time of day based on the user's past order history. This allows the system to suggest the optimal ordering method based on the user's past order history. Some or all of the above processes in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input the user's past order history into an AI, which can then suggest the optimal ordering method.

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

[0110] Step 1: The reception desk allows users to input conditions such as budget, family structure, nutritional needs, and dietary restrictions due to illness. For example, the reception desk provides an interface for users to input conditions via a messaging app. Step 2: The analysis unit analyzes the conditions entered by the reception unit. For example, the analysis unit uses AI to analyze the entered conditions and extract data to generate the optimal menu. Step 3: The generation unit generates a menu based on the conditions analyzed by the analysis unit. For example, the generation unit uses AI to generate a week's worth of menus. The generation unit suggests a menu for the week, such as oatmeal for breakfast on Monday, chicken salad for lunch, and grilled chicken for dinner. Step 4: The ordering unit orders ingredients based on the menu generated by the generation unit. For example, the ordering unit connects to an e-commerce site and automatically orders the necessary ingredients.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and ordering unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input conditions through a messaging app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the input conditions and extract data for generating an optimal menu. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate a week's worth of menus. The ordering unit is implemented by the control unit 46A of the smart device 14 and links to an e-commerce site to automatically order the necessary ingredients. 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.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and ordering unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input conditions through a messaging app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the input conditions and extract data for generating an optimal menu. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate a week's worth of menus. The ordering unit is implemented by the control unit 46A of the smart glasses 214 and links to an e-commerce site to automatically order the necessary ingredients. 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.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and ordering unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input conditions through a messaging application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the input conditions and extract data for generating an optimal menu. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate a week's worth of menus. The ordering unit is implemented by the control unit 46A of the headset terminal 314 and links to an e-commerce site to automatically order the necessary ingredients. 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.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and ordering 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 control unit 46A of the robot 414 and provides an interface for the user to input conditions through a messaging application. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the input conditions and extract data for generating an optimal menu. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to generate a week's worth of menus. The ordering unit is implemented by, for example, the control unit 46A of the robot 414 and links to an e-commerce site to automatically order the necessary ingredients. 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.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The reception desk where you enter the conditions, An analysis unit analyzes the conditions entered by the reception unit, A generation unit that generates a menu based on the conditions analyzed by the aforementioned analysis unit, The system includes an ordering unit that orders ingredients based on the menu generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter your budget, family structure, nutritional needs, and any dietary restrictions due to illness. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the conditions entered by the reception unit and generates the optimal menu. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a meal plan for one week. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering section is, The ingredients are ordered based on the menu generated by the generation unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned ordering section is, It connects to e-commerce sites and allows you to order groceries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the condition input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter conditions, the system automatically completes the input fields based on their current health status and lifestyle. 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 adjusts the priority of input fields based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their preferences, the system suggests local ingredients and dishes that are specific to their region, taking their geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter conditions, the system analyzes their social media activity and automatically fills in relevant conditions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by taking into account the user's health data and lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system takes the user's geographical location into account and incorporates region-specific ingredients and dishes into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant data is used for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the way the menu is presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating menus, the system refers to the user's past meal history to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating menus, the nutritional balance is optimized by taking into account the user's health data and lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the menu based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating menus, the system takes the user's geographical location into consideration and suggests local ingredients and dishes. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating menus, the system analyzes the user's social media activity and suggests relevant ingredients and dishes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering section is, When you place an order, we refer to your past order history to suggest the most suitable ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, When placing an order, the order content is optimized by taking into account the user's health data and lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, It estimates the user's emotions and adjusts how orders are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, When ordering, the system prioritizes ordering local ingredients and dishes based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering section is, When an order is placed, the system analyzes the user's social media activity and orders relevant ingredients and dishes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you enter the conditions, An analysis unit analyzes the conditions entered by the reception unit, A generation unit that generates a menu based on the conditions analyzed by the aforementioned analysis unit, The system includes an ordering unit that orders ingredients based on the menu generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter your budget, family structure, nutritional needs, and any dietary restrictions due to illness. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the conditions entered by the reception unit and generates the optimal menu. The system according to feature 1.

4. The generating unit is Generate a meal plan for one week. The system according to feature 1.

5. The aforementioned ordering section is, The ingredients are ordered based on the menu generated by the generation unit. The system according to feature 1.

6. The aforementioned ordering section is, It connects to e-commerce sites and allows you to order groceries. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the condition input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter conditions, the system automatically completes the input fields based on their current health status and lifestyle. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input fields based on the estimated emotions. The system according to feature 1.

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