system

Abstract

The system according to this embodiment aims to efficiently generate and provide meal menus for users. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a serving unit. The reception unit receives information about the user's meal menu. The generation unit generates a meal menu based on the information received by the reception unit. The serving unit provides the meal 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's meal menu has not been sufficiently generated and provided efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently generate and provide a user's meal menu.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information regarding the user's meal menu. The generation unit generates a meal menu based on the information received by the reception unit. The provision unit provides the meal menu generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently generate and provide meal menus for users. [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 household chore burden reduction and healthy eating support system according to an embodiment of the present invention is a system that reduces the burden of household chores and provides a healthy diet by utilizing an AI agent. This system begins with the user inputting information about meal menus. Next, the AI ​​agent utilizes expert knowledge to propose meal menus that meet the user's needs and preferences. In this process, the AI ​​agent considers the user's allergy information, diet goals, and family's eating preferences. Furthermore, using natural language processing technology, it analyzes preferences, health status, allergy information, etc., from the user's input and utilizes a large-scale language model to automatically generate nutritionally balanced recipes and weekly menus. In addition, by combining it with image recognition technology, it is possible to scan the contents of the refrigerator and food cabinets and propose recipes based on inventory. This mechanism can reduce the burden of household chores and support a healthy diet. For example, when the user inputs information about meal menus, the AI ​​agent proposes meal menus considering the user's allergy information, diet goals, and family's eating preferences. Furthermore, the AI ​​agent can scan the contents of the refrigerator and food cabinets and propose recipes based on inventory. This allows the user to prepare meals efficiently and maintain a healthy diet. This allows the system to reduce the burden of household chores and support a healthy diet, thereby alleviating the user's household chore burden and supporting a healthy diet.

[0029] The household chore burden reduction and healthy eating support system according to this embodiment comprises a reception unit, a generation unit, and a serving unit. The reception unit receives information about the user's meal menu. This information includes, but is not limited to, the types of ingredients, cooking methods, and calorie information. The reception unit receives, for example, meal menu information entered by the user in digital format. The reception unit can also receive meal menu information using voice input or image recognition technology. For example, the reception unit can obtain meal menu information by analyzing images taken by the user using a smartphone camera. The generation unit generates meal menus based on the information received by the reception unit using a generation AI. The generation unit generates meal menus considering, for example, the user's allergy information, diet goals, and family dietary preferences. The generation unit can also automatically generate nutritionally balanced recipes and weekly menus. For example, the generation unit analyzes the user's input information using a generation AI and generates nutritionally balanced meal menus. The serving unit provides the meal menus generated by the generation unit. The service provider can, for example, offer meal menus tailored to the user's preferences and health condition. It can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For instance, it can scan the ingredients in the refrigerator and suggest recipes based on that inventory information. Thus, the household chore reduction and healthy eating support system according to this embodiment can reduce the user's household chore burden and support a healthy eating lifestyle.

[0030] The reception desk receives information about the user's meal menu. This information may include, but is not limited to, the types of ingredients, cooking methods, and calorie information. The reception desk can, for example, receive meal menu information entered by the user in digital format. The reception desk can also receive meal menu information using voice input or image recognition technology. For example, the reception desk can obtain meal menu information by analyzing images taken by the user using their smartphone camera. Specifically, when the user opens a smartphone application and takes a picture of the ingredients, the image is sent to a server in the cloud. On the server, an image recognition algorithm identifies the ingredients and determines their type and quantity. Furthermore, by using the voice input function, the user can input the names of the ingredients and cooking methods by voice. Voice recognition technology converts the input voice into text data and incorporates it into the system. This allows users to easily provide meal menu information, and the system can receive the information accurately and quickly. The reception desk can also learn the user's past meal history and preferences to simplify input in the future. For example, it can have a function that remembers ingredients and cooking methods frequently used by the user and automatically suggests them the next time they input information. This further reduces the burden of input for the user and enables more efficient information gathering.

[0031] The generation unit uses a generation AI to generate meal menus based on information received by the reception unit. For example, the generation unit considers the user's allergy information, diet goals, and family dietary preferences when generating menus. The generation unit can also automatically generate nutritionally balanced recipes and weekly menus. For instance, the generation unit uses the generation AI to analyze user input and generate nutritionally balanced meal menus. Specifically, the generation AI proposes the optimal meal menu based on the user's provided ingredient information, cooking methods, and calorie information. The generation AI consults a vast recipe database to select the most suitable recipe based on the user's preferences and health condition. Furthermore, the generation AI considers the user's allergy information and restrictions on specific nutrients to generate safe and healthy menus. For example, if a user requests a gluten-free diet, the generation AI will suggest recipes using gluten-free ingredients. The generation AI also generates menus with adjusted calorie and nutrient balances according to the user's diet goals. For example, it will suggest low-calorie, high-protein menus for users aiming to lose weight, and high-protein, moderate-calorie menus for users aiming to build muscle. Furthermore, the generation AI can take into account the dietary preferences of all family members and generate menus that will satisfy everyone. This allows the generation unit to provide meal menus that meet the diverse needs of users, supporting a healthy eating lifestyle.

[0032] The service provider provides meal menus generated by the generation unit. For example, the service provider can provide meal menus tailored to the user's preferences and health condition. The service provider can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For example, the service provider can scan the ingredients in the refrigerator and suggest recipes based on that inventory information. Specifically, the service provider uses cameras and sensors installed in the refrigerator to grasp the types and quantities of ingredients in real time. This eliminates the need for the user to check the inventory in the refrigerator. Furthermore, the service provider displays the meal menu on the user's smartphone or tablet and provides cooking instructions and a list of necessary ingredients. The user can efficiently prepare meals based on the provided menu. The service provider can also record the user's meal history and use it to suggest menus for future use. For example, it can have a function to prioritize suggesting menus that the user has enjoyed eating in the past or menus that use a lot of specific ingredients. This allows the user to always enjoy new menus and maintain a healthy diet without getting bored. Furthermore, the service provider can collect user feedback and use it to improve menus and develop new recipes. For example, by allowing users to provide ratings and comments on the menus offered, the system can use that information to improve the accuracy and variety of the menus. This allows the service provider to consistently offer users the most suitable meal options and support a healthy diet.

[0033] The generation unit can generate meal menus using generation AI, taking into account the user's allergy information, diet goals, and family dietary preferences. For example, based on the user's allergy information, the generation unit can generate meal menus that do not contain allergens. It can also generate meal menus that consider calorie restriction and nutritional balance based on the user's diet goals. Furthermore, it can generate meal menus that satisfy the entire family based on the family's dietary preferences. For example, the generation unit can generate meal menus using ingredients that everyone in the family likes. In this way, by generating meal menus that take into account the user's allergy information, diet goals, and family dietary preferences, meals tailored to individual needs can be provided. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can generate meal menus using a generation AI model that takes the user's allergy information, diet goals, and family dietary preferences as input and outputs meal menus.

[0034] The generation unit can automatically generate nutritionally balanced recipes and weekly menus using generation AI. For example, to generate nutritionally balanced recipes, the generation unit considers the recommended intake of each nutrient. The generation unit can also create a one-week meal plan to generate weekly menus. For example, the generation unit considers the menu composition for each day to generate a balanced meal menu. In this way, by automatically generating nutritionally balanced recipes and weekly menus, it can support a healthy diet. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can generate meal menus using a generation AI model that takes nutritionally balanced recipes and weekly menus as input and outputs meal menus.

[0035] The service provider can offer meal menus tailored to the user's preferences and health condition. For example, the service provider can offer meal menus that take into account the user's favorite dishes and disliked ingredients. Furthermore, the service provider can offer meal menus fortified with specific nutrients based on the user's health condition. For example, the service provider can offer meal menus rich in vitamins and minerals according to the user's health condition. This allows the service provider to offer meals that meet individual needs by providing meal menus tailored to the user's preferences and health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can offer meal menus using an AI model that takes the user's preferences and health condition as input and outputs meal menus.

[0036] The service provider can scan the contents of refrigerators and food cabinets to suggest recipes based on inventory. For example, it can scan the ingredients in a refrigerator and suggest recipes based on that inventory information. It can also scan the contents of food cabinets and suggest combinations of available ingredients. For example, it can acquire information about ingredients using barcode scanning or image recognition technology and suggest recipes based on that information. By scanning the contents of refrigerators and food cabinets and suggesting recipes based on inventory, it is possible to reduce food waste and support efficient meal preparation. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can suggest recipes using an AI model that takes the contents of refrigerators and food cabinets as input and outputs recipes.

[0037] The service provider can leverage expert knowledge to provide users with optimal meal plans. For example, the service provider can provide meal plans based on advice from nutritionists or recommendations from doctors. It can also provide meal plans based on recipes from culinary researchers. For instance, the service provider can leverage expert knowledge to provide nutritionally balanced meal plans. This allows for the provision of healthier and more balanced meals by leveraging expert knowledge to provide users with optimal meal plans. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can provide meal plans using an AI model that takes expert knowledge as input and outputs meal plans.

[0038] The reception desk can analyze the user's past meal history and select the optimal information input method. For example, the reception desk can automatically display meal menus that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest meal menus to be used at specific times based on the user's past meal history. This improves the efficiency of information input by analyzing the user's past meal history and selecting the optimal information input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can select an information input method using an AI model that takes the user's past meal history as input and outputs the optimal information input method.

[0039] The reception unit can filter menu information based on the user's current health status and dietary preferences. For example, the reception unit can exclude menus containing specific ingredients based on the user's health status. It can also prioritize displaying menus containing preferred ingredients based on the user's dietary preferences. Furthermore, the reception unit can exclude menus containing allergens based on the user's allergy information. This allows for the suggestion of more appropriate menus by filtering based on the user's health status and dietary preferences. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can perform filtering using an AI model that takes the user's health status and dietary preferences as input and outputs filtering results.

[0040] The reception desk can prioritize inputting highly relevant information when entering meal menu information, taking into account the user's geographical location. For example, the reception desk can suggest menus using ingredients available at nearby grocery stores based on the user's current location. It can also suggest menus using local specialties based on the user's geographical location. Furthermore, it can suggest menus using seasonal ingredients based on the user's geographical location. By prioritizing the input of highly relevant information while considering the user's geographical location, it can suggest more appropriate menus. 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 information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when inputting information about meal menus. For example, the reception desk can suggest relevant recipes based on meal menus the user has shared on social media. It can also suggest relevant recipes based on meal menus preferred by the user's social media followers. Furthermore, it can suggest relevant recipes based on meal menus the user has "liked" on social media. In this way, by analyzing the user's social media activity and inputting relevant information, it is possible to suggest more appropriate menus. 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 information using an AI model that takes the user's social media activity as input and outputs relevant information.

[0042] The generation unit can adjust the level of detail generated when creating meal menus based on the user's allergy information and diet goals. For example, the generation unit can generate menus that do not contain allergens based on the user's allergy information. It can also generate menus that take into account calorie restrictions and nutritional balance based on the user's diet goals. Furthermore, it can generate menus that are fortified with specific nutrients based on the user's health condition. By adjusting the level of detail generated based on the user's allergy information and diet goals, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can adjust the level of detail generated using a generation AI model that takes the user's allergy information and diet goals as input and outputs the level of detail of the generated menu.

[0043] The generation unit can apply different generation algorithms to meal menus according to the dietary preferences of the user's family. For example, the generation unit can generate menus using ingredients that everyone in the family likes. It can also generate menus that exclude ingredients that some family members avoid. Furthermore, if a family member has a specific dietary style (vegetarian, gluten-free, etc.), the generation unit can generate menus tailored to that style. This allows the system to generate menus that satisfy everyone in the family by applying different generation algorithms according to their dietary preferences. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the user's family's dietary preferences as input and outputs a generation algorithm.

[0044] The generation unit can determine the priority of meal menus based on the user's eating history. For example, the generation unit can prioritize generating menus that the user has enjoyed eating in the past. It can also exclude menus that the user has avoided in the past. Furthermore, the generation unit can prioritize generating menus that supplement specific nutrients based on the user's eating history. In this way, by determining the priority of menus based on the user's eating history, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can determine the priority of menus using a generation AI model that takes the user's eating history as input and outputs the priority of menus.

[0045] The generation unit can adjust the order of menu generation based on the user's health condition. For example, if the user is unwell, the generation unit will prioritize generating easily digestible menus. It can also prioritize generating menus suitable for energy replenishment if the user is seeking healthy exercise. Furthermore, if the user is tired, the generation unit can prioritize generating highly nutritious menus. By adjusting the order of generation based on the user's health condition, a more appropriate menu can be generated. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the order of generation using a generation AI model that takes the user's health condition as input and outputs the generation order.

[0046] The service provider can select the optimal service method when serving meals by referring to the user's past meal history. For example, the service provider can prioritize serving meals that the user has enjoyed in the past. It can also exclude meals that the user has avoided in the past. Furthermore, based on the user's meal history, the service provider can prioritize serving meals that supplement specific nutrients. By selecting the optimal service method by referring to the user's past meal history, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's past meal history as input and outputs a service method.

[0047] The service provider can customize the means of serving meals based on the user's current health condition. For example, if the user is feeling unwell, the service provider may prioritize serving easily digestible meals. Similarly, if the user is seeking healthy exercise, the service provider may prioritize serving meals suitable for energy replenishment. Furthermore, if the user is fatigued, the service provider may prioritize serving meals with high nutritional value. This allows for the provision of more appropriate meals by customizing the means of serving based on the user's current health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider may customize the means of serving using an AI model that takes the user's health condition as input and outputs the means of serving.

[0048] The service provider can select the optimal service method when providing a meal menu, taking into account the user's geographical location. For example, the service provider can suggest a menu using ingredients available at nearby grocery stores based on the user's current location. It can also suggest a menu using local specialties based on the user's geographical location. Furthermore, it can suggest a menu using seasonal ingredients based on the user's geographical location. By selecting the optimal service method considering the user's geographical location, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's geographical location as input and outputs a service method.

[0049] The service provider can analyze the user's social media activity and propose a means of service when providing a meal menu. For example, the service provider can propose relevant recipes based on meal menus shared by the user on social media. It can also propose relevant recipes based on meal menus preferred by the user's social media followers. Furthermore, it can propose relevant recipes based on meal menus that the user has "liked" on social media. By analyzing the user's social media activity and proposing a means of service, the service provider can offer a more appropriate menu. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can propose a means of service using an AI model that takes the user's social media activity as input and outputs a means of service.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze the user's past meal history and select the optimal method of information input. For example, it can automatically display meal menus that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest meal menus that the user will use at a specific time of day based on their past meal history. This improves the efficiency of information input by analyzing the user's past meal history and selecting the optimal method of information input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select the information input method using an AI model that takes the user's past meal history as input and outputs the optimal method of information input.

[0052] The service provider can select the optimal service method when serving meals by referring to the user's past eating history. For example, it can prioritize serving meals that the user has enjoyed in the past. It can also exclude meals that the user has avoided in the past. Furthermore, it can prioritize serving meals that supplement specific nutrients based on the user's eating history. By selecting the optimal service method by referring to the user's past eating history, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's past eating history as input and outputs a service method.

[0053] The generation unit can adjust the level of detail generated when creating meal menus based on the user's allergy information and diet goals. For example, it can generate menus that do not contain allergens based on the user's allergy information. It can also generate menus that take into account calorie restrictions and nutritional balance based on the user's diet goals. Furthermore, it can generate menus that are fortified with specific nutrients based on the user's health condition. In this way, by adjusting the level of detail generated based on the user's allergy information and diet goals, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can adjust the level of detail generated using a generation AI model that takes the user's allergy information and diet goals as input and outputs the level of detail of the generated menu.

[0054] The reception desk can prioritize inputting highly relevant information when entering meal menu information, taking into account the user's geographical location. For example, it can suggest menus using ingredients available at nearby grocery stores based on the user's current location. It can also suggest menus using local specialties based on the user's geographical location. Furthermore, it can suggest menus using seasonal ingredients based on the user's geographical location. By prioritizing the input of highly relevant information while considering the user's geographical location, it can suggest more appropriate menus. 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 information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0055] The generation unit can apply different generation algorithms to meal menus according to the dietary preferences of the user's family. For example, it can generate menus using ingredients that everyone in the family likes. It can also generate menus that exclude ingredients that some family members avoid. Furthermore, if some family members have a specific dietary style (vegetarian, gluten-free, etc.), it can generate menus tailored to that style. By applying different generation algorithms according to the dietary preferences of the user's family, it is possible to generate menus that satisfy everyone in the family. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the dietary preferences of the user's family as input and outputs a generation algorithm.

[0056] The service department can select the optimal service method when providing a meal menu, taking into account the user's geographical location. For example, it can suggest a menu using ingredients available at nearby grocery stores based on the user's current location. It can also suggest a menu using local specialties based on the user's geographical location. Furthermore, it can suggest a menu using seasonal ingredients based on the user's geographical location. By selecting the optimal service method considering the user's geographical location, a more appropriate menu can be provided. Some or all of the above processing in the service department may be performed using AI, or not. For example, the service department can select a service method using an AI model that takes the user's geographical location as input and outputs a service method.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives information about the user's meal menu. This information includes, for example, the types of ingredients, cooking methods, and calorie information. The reception desk accepts the meal menu information entered by the user in digital format, and can also accept it using voice input or image recognition technology. For example, a user can take a picture of the ingredients using their smartphone camera, and the system can analyze that image to obtain the meal menu information. Step 2: The generation unit uses generation AI to generate meal menus based on the information received by the reception unit. The generation unit generates meal menus considering the user's allergy information, diet goals, and family dietary preferences. It can also automatically generate nutritionally balanced recipes and weekly menus. Step 3: The serving unit provides the meal menu generated by the generation unit. The serving unit provides meal menus tailored to the user's preferences and health condition, and can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For example, it can scan the ingredients in the refrigerator and suggest recipes based on that inventory information.

[0059] (Example of form 2) The household chore burden reduction and healthy eating support system according to an embodiment of the present invention is a system that reduces the burden of household chores and provides a healthy diet by utilizing an AI agent. This system begins with the user inputting information about meal menus. Next, the AI ​​agent utilizes expert knowledge to propose meal menus that meet the user's needs and preferences. In this process, the AI ​​agent considers the user's allergy information, diet goals, and family's eating preferences. Furthermore, using natural language processing technology, it analyzes preferences, health status, allergy information, etc., from the user's input and utilizes a large-scale language model to automatically generate nutritionally balanced recipes and weekly menus. In addition, by combining it with image recognition technology, it is possible to scan the contents of the refrigerator and food cabinets and propose recipes based on inventory. This mechanism can reduce the burden of household chores and support a healthy diet. For example, when the user inputs information about meal menus, the AI ​​agent proposes meal menus considering the user's allergy information, diet goals, and family's eating preferences. Furthermore, the AI ​​agent can scan the contents of the refrigerator and food cabinets and propose recipes based on inventory. This allows the user to prepare meals efficiently and maintain a healthy diet. This allows the system to reduce the burden of household chores and support a healthy diet, thereby alleviating the user's household chore burden and supporting a healthy diet.

[0060] The household chore burden reduction and healthy eating support system according to this embodiment comprises a reception unit, a generation unit, and a serving unit. The reception unit receives information about the user's meal menu. This information includes, but is not limited to, the types of ingredients, cooking methods, and calorie information. The reception unit receives, for example, meal menu information entered by the user in digital format. The reception unit can also receive meal menu information using voice input or image recognition technology. For example, the reception unit can obtain meal menu information by analyzing images taken by the user using a smartphone camera. The generation unit generates meal menus based on the information received by the reception unit using a generation AI. The generation unit generates meal menus considering, for example, the user's allergy information, diet goals, and family dietary preferences. The generation unit can also automatically generate nutritionally balanced recipes and weekly menus. For example, the generation unit analyzes the user's input information using a generation AI and generates nutritionally balanced meal menus. The serving unit provides the meal menus generated by the generation unit. The service provider can, for example, offer meal menus tailored to the user's preferences and health condition. It can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For instance, it can scan the ingredients in the refrigerator and suggest recipes based on that inventory information. Thus, the household chore reduction and healthy eating support system according to this embodiment can reduce the user's household chore burden and support a healthy eating lifestyle.

[0061] The reception desk receives information about the user's meal menu. This information may include, but is not limited to, the types of ingredients, cooking methods, and calorie information. The reception desk can, for example, receive meal menu information entered by the user in digital format. The reception desk can also receive meal menu information using voice input or image recognition technology. For example, the reception desk can obtain meal menu information by analyzing images taken by the user using their smartphone camera. Specifically, when the user opens a smartphone application and takes a picture of the ingredients, the image is sent to a server in the cloud. On the server, an image recognition algorithm identifies the ingredients and determines their type and quantity. Furthermore, by using the voice input function, the user can input the names of the ingredients and cooking methods by voice. Voice recognition technology converts the input voice into text data and incorporates it into the system. This allows users to easily provide meal menu information, and the system can receive the information accurately and quickly. The reception desk can also learn the user's past meal history and preferences to simplify input in the future. For example, it can have a function that remembers ingredients and cooking methods frequently used by the user and automatically suggests them the next time they input information. This further reduces the burden of input for the user and enables more efficient information gathering.

[0062] The generation unit uses a generation AI to generate meal menus based on information received by the reception unit. For example, the generation unit considers the user's allergy information, diet goals, and family dietary preferences when generating menus. The generation unit can also automatically generate nutritionally balanced recipes and weekly menus. For instance, the generation unit uses the generation AI to analyze user input and generate nutritionally balanced meal menus. Specifically, the generation AI proposes the optimal meal menu based on the user's provided ingredient information, cooking methods, and calorie information. The generation AI consults a vast recipe database to select the most suitable recipe based on the user's preferences and health condition. Furthermore, the generation AI considers the user's allergy information and restrictions on specific nutrients to generate safe and healthy menus. For example, if a user requests a gluten-free diet, the generation AI will suggest recipes using gluten-free ingredients. The generation AI also generates menus with adjusted calorie and nutrient balances according to the user's diet goals. For example, it will suggest low-calorie, high-protein menus for users aiming to lose weight, and high-protein, moderate-calorie menus for users aiming to build muscle. Furthermore, the generation AI can take into account the dietary preferences of all family members and generate menus that will satisfy everyone. This allows the generation unit to provide meal menus that meet the diverse needs of users, supporting a healthy eating lifestyle.

[0063] The service provider provides meal menus generated by the generation unit. For example, the service provider can provide meal menus tailored to the user's preferences and health condition. The service provider can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For example, the service provider can scan the ingredients in the refrigerator and suggest recipes based on that inventory information. Specifically, the service provider uses cameras and sensors installed in the refrigerator to grasp the types and quantities of ingredients in real time. This eliminates the need for the user to check the inventory in the refrigerator. Furthermore, the service provider displays the meal menu on the user's smartphone or tablet and provides cooking instructions and a list of necessary ingredients. The user can efficiently prepare meals based on the provided menu. The service provider can also record the user's meal history and use it to suggest menus for future use. For example, it can have a function to prioritize suggesting menus that the user has enjoyed eating in the past or menus that use a lot of specific ingredients. This allows the user to always enjoy new menus and maintain a healthy diet without getting bored. Furthermore, the service provider can collect user feedback and use it to improve menus and develop new recipes. For example, by allowing users to provide ratings and comments on the menus offered, the system can use that information to improve the accuracy and variety of the menus. This allows the service provider to consistently offer users the most suitable meal options and support a healthy diet.

[0064] The generation unit can generate meal menus using generation AI, taking into account the user's allergy information, diet goals, and family dietary preferences. For example, based on the user's allergy information, the generation unit can generate meal menus that do not contain allergens. It can also generate meal menus that consider calorie restriction and nutritional balance based on the user's diet goals. Furthermore, it can generate meal menus that satisfy the entire family based on the family's dietary preferences. For example, the generation unit can generate meal menus using ingredients that everyone in the family likes. In this way, by generating meal menus that take into account the user's allergy information, diet goals, and family dietary preferences, meals tailored to individual needs can be provided. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can generate meal menus using a generation AI model that takes the user's allergy information, diet goals, and family dietary preferences as input and outputs meal menus.

[0065] The generation unit can automatically generate nutritionally balanced recipes and weekly menus using generation AI. For example, to generate nutritionally balanced recipes, the generation unit considers the recommended intake of each nutrient. The generation unit can also create a one-week meal plan to generate weekly menus. For example, the generation unit considers the menu composition for each day to generate a balanced meal menu. In this way, by automatically generating nutritionally balanced recipes and weekly menus, it can support a healthy diet. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can generate meal menus using a generation AI model that takes nutritionally balanced recipes and weekly menus as input and outputs meal menus.

[0066] The service provider can offer meal menus tailored to the user's preferences and health condition. For example, the service provider can offer meal menus that take into account the user's favorite dishes and disliked ingredients. Furthermore, the service provider can offer meal menus fortified with specific nutrients based on the user's health condition. For example, the service provider can offer meal menus rich in vitamins and minerals according to the user's health condition. This allows the service provider to offer meals that meet individual needs by providing meal menus tailored to the user's preferences and health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can offer meal menus using an AI model that takes the user's preferences and health condition as input and outputs meal menus.

[0067] The service provider can scan the contents of refrigerators and food cabinets to suggest recipes based on inventory. For example, it can scan the ingredients in a refrigerator and suggest recipes based on that inventory information. It can also scan the contents of food cabinets and suggest combinations of available ingredients. For example, it can acquire information about ingredients using barcode scanning or image recognition technology and suggest recipes based on that information. By scanning the contents of refrigerators and food cabinets and suggesting recipes based on inventory, it is possible to reduce food waste and support efficient meal preparation. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can suggest recipes using an AI model that takes the contents of refrigerators and food cabinets as input and outputs recipes.

[0068] The service provider can leverage expert knowledge to provide users with optimal meal plans. For example, the service provider can provide meal plans based on advice from nutritionists or recommendations from doctors. It can also provide meal plans based on recipes from culinary researchers. For instance, the service provider can leverage expert knowledge to provide nutritionally balanced meal plans. This allows for the provision of healthier and more balanced meals by leveraging expert knowledge to provide users with optimal meal plans. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can provide meal plans using an AI model that takes expert knowledge as input and outputs meal plans.

[0069] The reception desk can estimate the user's emotions and adjust the timing of information input for the meal menu based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to input information for the meal menu during a time when they can relax. The reception desk can also provide a simplified input form for users who are busy, allowing for quicker input. Furthermore, if the user is relaxed, the reception desk can offer detailed input options and suggest customizable input methods. This allows for more appropriate information input at the right time by adjusting the timing of meal menu information input 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 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 adjust the information input timing using an AI model that takes user emotion data as input and outputs the information input timing.

[0070] The reception desk can analyze the user's past meal history and select the optimal information input method. For example, the reception desk can automatically display meal menus that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest meal menus to be used at specific times based on the user's past meal history. This improves the efficiency of information input by analyzing the user's past meal history and selecting the optimal information input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can select an information input method using an AI model that takes the user's past meal history as input and outputs the optimal information input method.

[0071] The reception unit can filter menu information based on the user's current health status and dietary preferences. For example, the reception unit can exclude menus containing specific ingredients based on the user's health status. It can also prioritize displaying menus containing preferred ingredients based on the user's dietary preferences. Furthermore, the reception unit can exclude menus containing allergens based on the user's allergy information. This allows for the suggestion of more appropriate menus by filtering based on the user's health status and dietary preferences. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can perform filtering using an AI model that takes the user's health status and dietary preferences as input and outputs filtering results.

[0072] The reception desk can estimate the user's emotions and determine the priority of the food menu information to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize displaying food menus with relaxing effects. It can also prioritize displaying highly nutritious food menus if the user is tired. Furthermore, if the user is feeling energetic, it can prioritize displaying new ingredients and recipes. This allows for the prioritization of more appropriate menus by determining the priority of food menu information based on 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 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 determine the priority of information using an AI model that takes user emotion data as input and outputs the priority of information.

[0073] The reception desk can prioritize inputting highly relevant information when entering meal menu information, taking into account the user's geographical location. For example, the reception desk can suggest menus using ingredients available at nearby grocery stores based on the user's current location. It can also suggest menus using local specialties based on the user's geographical location. Furthermore, it can suggest menus using seasonal ingredients based on the user's geographical location. By prioritizing the input of highly relevant information while considering the user's geographical location, it can suggest more appropriate menus. 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 information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0074] The reception desk can analyze the user's social media activity and input relevant information when inputting information about meal menus. For example, the reception desk can suggest relevant recipes based on meal menus the user has shared on social media. It can also suggest relevant recipes based on meal menus preferred by the user's social media followers. Furthermore, it can suggest relevant recipes based on meal menus the user has "liked" on social media. In this way, by analyzing the user's social media activity and inputting relevant information, it is possible to suggest more appropriate menus. 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 information using an AI model that takes the user's social media activity as input and outputs relevant information.

[0075] The generation unit can estimate the user's emotions and adjust the method of generating the meal menu based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a meal menu that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a meal menu that can be prepared in a short time. Furthermore, if the user is excited, the generation unit can generate a meal menu with visually stimulating effects. In this way, by adjusting the method of generating the meal menu based on the user's emotions, a more appropriate menu can be generated. 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 or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can adjust the generation method using a generative AI model that takes user emotion data as input and outputs a generation method.

[0076] The generation unit can adjust the level of detail generated when creating meal menus based on the user's allergy information and diet goals. For example, the generation unit can generate menus that do not contain allergens based on the user's allergy information. It can also generate menus that take into account calorie restrictions and nutritional balance based on the user's diet goals. Furthermore, it can generate menus that are fortified with specific nutrients based on the user's health condition. By adjusting the level of detail generated based on the user's allergy information and diet goals, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can adjust the level of detail generated using a generation AI model that takes the user's allergy information and diet goals as input and outputs the level of detail of the generated menu.

[0077] The generation unit can apply different generation algorithms to meal menus according to the dietary preferences of the user's family. For example, the generation unit can generate menus using ingredients that everyone in the family likes. It can also generate menus that exclude ingredients that some family members avoid. Furthermore, if a family member has a specific dietary style (vegetarian, gluten-free, etc.), the generation unit can generate menus tailored to that style. This allows the system to generate menus that satisfy everyone in the family by applying different generation algorithms according to their dietary preferences. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the user's family's dietary preferences as input and outputs a generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of the meal menu it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate a menu that can be prepared quickly. If the user is relaxed, the generation unit can also generate a menu that can be enjoyed at a leisurely pace. Furthermore, if the user is excited, the generation unit can generate a menu with visually stimulating effects. By adjusting the length of the meal menu based on the user's emotions, a more appropriate menu can be generated. 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 or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit can adjust the length of the menu using a generative AI model that takes user emotion data as input and outputs the menu length.

[0079] The generation unit can determine the priority of meal menus based on the user's eating history. For example, the generation unit can prioritize generating menus that the user has enjoyed eating in the past. It can also exclude menus that the user has avoided in the past. Furthermore, the generation unit can prioritize generating menus that supplement specific nutrients based on the user's eating history. In this way, by determining the priority of menus based on the user's eating history, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can determine the priority of menus using a generation AI model that takes the user's eating history as input and outputs the priority of menus.

[0080] The generation unit can adjust the order of menu generation based on the user's health condition. For example, if the user is unwell, the generation unit will prioritize generating easily digestible menus. It can also prioritize generating menus suitable for energy replenishment if the user is seeking healthy exercise. Furthermore, if the user is tired, the generation unit can prioritize generating highly nutritious menus. By adjusting the order of generation based on the user's health condition, a more appropriate menu can be generated. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the order of generation using a generation AI model that takes the user's health condition as input and outputs the generation order.

[0081] The service provider can estimate the user's emotions and adjust the way the meal menu is served based on those emotions. For example, if the user is relaxed, the service provider can serve a meal menu that proceeds at a leisurely pace. If the user is in a hurry, the service provider can also serve a meal menu that can be prepared quickly. Furthermore, if the user is excited, the service provider can serve a meal menu with visually stimulating effects. By adjusting the way the meal menu is served based on the user's emotions, a more appropriate menu can be provided. 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 or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can adjust the serving method using an AI model that takes user emotion data as input and outputs a serving method.

[0082] The service provider can select the optimal service method when serving meals by referring to the user's past meal history. For example, the service provider can prioritize serving meals that the user has enjoyed in the past. It can also exclude meals that the user has avoided in the past. Furthermore, based on the user's meal history, the service provider can prioritize serving meals that supplement specific nutrients. By selecting the optimal service method by referring to the user's past meal history, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's past meal history as input and outputs a service method.

[0083] The service provider can customize the means of serving meals based on the user's current health condition. For example, if the user is feeling unwell, the service provider may prioritize serving easily digestible meals. Similarly, if the user is seeking healthy exercise, the service provider may prioritize serving meals suitable for energy replenishment. Furthermore, if the user is fatigued, the service provider may prioritize serving meals with high nutritional value. This allows for the provision of more appropriate meals by customizing the means of serving based on the user's current health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider may customize the means of serving using an AI model that takes the user's health condition as input and outputs the means of serving.

[0084] The service provider can estimate the user's emotions and determine the priority of the meal menu based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing meals with relaxing effects. It may also prioritize providing highly nutritious meals if the user is tired. Furthermore, if the user is energetic, the service provider may prioritize providing new ingredients or recipes. This allows for the provision of more appropriate menus by prioritizing meal menus based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can determine priorities using an AI model that takes user emotion data as input and outputs the priority of meal menus to be offered.

[0085] The service provider can select the optimal service method when providing a meal menu, taking into account the user's geographical location. For example, the service provider can suggest a menu using ingredients available at nearby grocery stores based on the user's current location. It can also suggest a menu using local specialties based on the user's geographical location. Furthermore, it can suggest a menu using seasonal ingredients based on the user's geographical location. By selecting the optimal service method considering the user's geographical location, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's geographical location as input and outputs a service method.

[0086] The service provider can analyze the user's social media activity and propose a means of service when providing a meal menu. For example, the service provider can propose relevant recipes based on meal menus shared by the user on social media. It can also propose relevant recipes based on meal menus preferred by the user's social media followers. Furthermore, it can propose relevant recipes based on meal menus that the user has "liked" on social media. By analyzing the user's social media activity and proposing a means of service, the service provider can offer a more appropriate menu. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can propose a means of service using an AI model that takes the user's social media activity as input and outputs a means of service.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The reception desk can estimate the user's emotions and adjust the timing of information input for the meal menu based on the estimated emotions. For example, if the user is feeling stressed, it can prompt them to input information for the meal menu during a time when they can relax. If the user is busy, it can provide a simplified input form to allow for quick input. Furthermore, if the user is relaxed, it can provide detailed input options and suggest customizable input methods. This allows for information to be entered at a more appropriate time by adjusting the timing of information input for the meal menu 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 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 adjust the information input timing using an AI model that takes user emotion data as input and outputs the information input timing.

[0089] The generation unit can estimate the user's emotions and adjust the method of generating meal menus based on the estimated emotions. For example, if the user is relaxed, it can generate meal menus that proceed at a leisurely pace. If the user is in a hurry, it can also generate meal menus that can be prepared in a short time. Furthermore, if the user is excited, it can generate meal menus with visually stimulating effects. In this way, by adjusting the method of generating meal menus based on the user's emotions, more appropriate menus can be generated. 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 or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generative AI. For example, the generation unit can adjust the generation method using a generative AI model that takes user emotion data as input and outputs a generation method.

[0090] The service unit can estimate the user's emotions and adjust the way the meal menu is served based on those emotions. For example, if the user is relaxed, it can serve a meal menu that proceeds at a leisurely pace. If the user is in a hurry, it can serve a meal menu that can be prepared quickly. Furthermore, if the user is excited, it can serve a meal menu with visually stimulating effects. By adjusting the way the meal menu is served based on the user's emotions, a more appropriate menu can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the service unit may be performed using AI or not. For example, the service unit can adjust the serving method using an AI model that takes user emotion data as input and outputs a serving method.

[0091] The reception desk can analyze the user's past meal history and select the optimal method of information input. For example, it can automatically display meal menus that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest meal menus that the user will use at a specific time of day based on their past meal history. This improves the efficiency of information input by analyzing the user's past meal history and selecting the optimal method of information input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select the information input method using an AI model that takes the user's past meal history as input and outputs the optimal method of information input.

[0092] The service provider can select the optimal service method when serving meals by referring to the user's past eating history. For example, it can prioritize serving meals that the user has enjoyed in the past. It can also exclude meals that the user has avoided in the past. Furthermore, it can prioritize serving meals that supplement specific nutrients based on the user's eating history. By selecting the optimal service method by referring to the user's past eating history, a more appropriate menu can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a service method using an AI model that takes the user's past eating history as input and outputs a service method.

[0093] The generation unit can adjust the level of detail generated when creating meal menus based on the user's allergy information and diet goals. For example, it can generate menus that do not contain allergens based on the user's allergy information. It can also generate menus that take into account calorie restrictions and nutritional balance based on the user's diet goals. Furthermore, it can generate menus that are fortified with specific nutrients based on the user's health condition. In this way, by adjusting the level of detail generated based on the user's allergy information and diet goals, more appropriate menus can be generated. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can adjust the level of detail generated using a generation AI model that takes the user's allergy information and diet goals as input and outputs the level of detail of the generated menu.

[0094] The service provider can estimate the user's emotions and determine the priority of the meal menu based on those emotions. For example, if the user is stressed, it can prioritize providing meals with relaxing effects. If the user is tired, it can prioritize providing meals with high nutritional value. Furthermore, if the user is energetic, it can prioritize providing new ingredients or recipes. By prioritizing the meal menu based on the user's emotions, a more appropriate menu can be provided. 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 or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can determine the priority using an AI model that takes user emotion data as input and outputs the priority of the meal menu to be offered.

[0095] The reception desk can prioritize inputting highly relevant information when entering meal menu information, taking into account the user's geographical location. For example, it can suggest menus using ingredients available at nearby grocery stores based on the user's current location. It can also suggest menus using local specialties based on the user's geographical location. Furthermore, it can suggest menus using seasonal ingredients based on the user's geographical location. By prioritizing the input of highly relevant information while considering the user's geographical location, it can suggest more appropriate menus. 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 information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0096] The generation unit can apply different generation algorithms to meal menus according to the dietary preferences of the user's family. For example, it can generate menus using ingredients that everyone in the family likes. It can also generate menus that exclude ingredients that some family members avoid. Furthermore, if some family members have a specific dietary style (vegetarian, gluten-free, etc.), it can generate menus tailored to that style. By applying different generation algorithms according to the dietary preferences of the user's family, it is possible to generate menus that satisfy everyone in the family. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the dietary preferences of the user's family as input and outputs a generation algorithm.

[0097] The service department can select the optimal service method when providing a meal menu, taking into account the user's geographical location. For example, it can suggest a menu using ingredients available at nearby grocery stores based on the user's current location. It can also suggest a menu using local specialties based on the user's geographical location. Furthermore, it can suggest a menu using seasonal ingredients based on the user's geographical location. By selecting the optimal service method considering the user's geographical location, a more appropriate menu can be provided. Some or all of the above processing in the service department may be performed using AI, or not. For example, the service department can select a service method using an AI model that takes the user's geographical location as input and outputs a service method.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives information about the user's meal menu. This information includes, for example, the types of ingredients, cooking methods, and calorie information. The reception desk accepts the meal menu information entered by the user in digital format, and can also accept it using voice input or image recognition technology. For example, a user can take a picture of the ingredients using their smartphone camera, and the system can analyze that image to obtain the meal menu information. Step 2: The generation unit uses generation AI to generate meal menus based on the information received by the reception unit. The generation unit generates meal menus considering the user's allergy information, diet goals, and family dietary preferences. It can also automatically generate nutritionally balanced recipes and weekly menus. Step 3: The serving unit provides the meal menu generated by the generation unit. The serving unit provides meal menus tailored to the user's preferences and health condition, and can also scan the contents of the refrigerator and food cabinets to suggest recipes based on inventory. For example, it can scan the ingredients in the refrigerator and suggest recipes based on that inventory information.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 receives the meal menu information entered by the user in digital format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the meal menu using a generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated meal menu to the user. 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.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements, including the reception unit, generation unit, and provision unit described above, 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 receives the meal menu information entered by the user in digital format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the meal menu using generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated meal menu to the user. 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.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 receives the meal menu information entered by the user in digital format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the meal menu using a generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated meal menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, and serving 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 receives the meal menu information entered by the user in digital format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the meal menu using a generation AI. The serving unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated meal menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A reception desk that receives information about users' meal menus, A generation unit that generates a meal menu based on the information received by the reception unit, The system includes a serving unit that provides the meal menu generated by the generating unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates meal plans that take into account the user's allergy information, diet goals, and family dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates nutritionally balanced recipes and weekly menus automatically. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides meal menus tailored to the user's preferences and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Scan the contents of your refrigerator and food cabinets to suggest recipes based on your inventory. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We leverage expert knowledge to provide users with the most suitable meal menu. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of meal menu information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past meal history and selects the optimal method for inputting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information about a meal menu, filtering is performed based on the user's current health status and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the food menu information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information for a meal menu, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information for a meal menu, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the meal menu generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating meal menus, the level of detail is adjusted based on the user's allergy information and diet goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating meal menus, different generation algorithms are applied depending on the dietary preferences of the user's family. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the meal menu generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating meal menus, the system prioritizes the creation of menus based on the user's meal history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating meal menus, the order of creation is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way the meal menu is served based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing a meal menu, the system selects the optimal serving method by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing meal menus, the method of delivery is customized based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of the meal menu based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing meal menus, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing meal menus, we analyze users' social media activity and suggest methods for delivery. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information about users' meal menus, A generation unit that generates a meal menu based on the information received by the reception unit, The system includes a serving unit that provides the meal menu generated by the generating unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates meal menus that take into account the user's allergy information, diet goals, and family's food preferences. The system according to feature 1.

3. The generating unit is The AI ​​generates nutritionally balanced recipes and weekly menus automatically. The system according to feature 1.

4. The aforementioned supply unit is, Provides meal menus tailored to the user's preferences and health condition. The system according to feature 1.

5. The aforementioned supply unit is, Scan the contents of your refrigerator and food cabinets to suggest recipes based on your inventory. The system according to feature 1.

6. The aforementioned supply unit is, We leverage expert knowledge to provide users with the most suitable meal menu. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of meal menu information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past meal history and selects the optimal method for inputting information. The system according to feature 1.

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