system

Abstract

The system according to this embodiment aims to support efficient and healthy lunch preparation by suggesting nutritionally balanced recipes based on the user's preferences and allergy information. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, a display unit, and a recording unit. The reception unit receives input from the user regarding preferences and allergy information. The suggestion unit proposes a nutritionally balanced recipe based on the information entered by the reception unit. The display unit displays the cooking procedure and a list of ingredients to purchase for the recipe proposed by the suggestion unit. The recording unit records the bento box that has been prepared.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to manage menu ideas and nutritional balance in daily bento making.

[0005] The system according to the embodiment aims to propose a recipe with a balanced nutrition based on the user's preferences and allergy information and support efficient and healthy bento making.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a display unit, and a recording unit. The reception unit receives user preferences and allergy information. The suggestion unit suggests a nutritionally balanced recipe based on the information entered by the reception unit. The display unit displays the cooking procedure and a list of ingredients to purchase for the recipe suggested by the suggestion unit. The recording unit records the bento box that has been prepared. [Effects of the Invention]

[0007] The system according to this embodiment can suggest nutritionally balanced recipes based on the user's preferences and allergy information, and can support the efficient and healthy preparation of packed lunches. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The lunchbox preparation support system according to an embodiment of the present invention is an integrated solution that supports parents who struggle with preparing lunchboxes every day. This lunchbox preparation support system solves challenges faced by parents, such as "lack of menu ideas," "managing nutritional balance," and "wanting to prepare lunches quickly during busy mornings," using an app equipped with AI functionality. This function proposes lunchbox menus every day that take into account nutritional balance, ease of preparation, and visual appeal, and automatically generates a weekly menu plan and shopping list. In addition, it allows users to record the lunchboxes they have made with photos and check past menu history and nutritional balance. Furthermore, it analyzes the nutrients necessary for growing children and proposes menus based on that. For example, a user accesses the app and enters their preferences and allergy information. Next, the AI ​​proposes a nutritionally balanced recipe based on this information. The proposed recipe is displayed along with cooking instructions and a list of ingredients to purchase. Furthermore, users can record the lunchboxes they have made with photos and check past menu history and nutritional balance. As a result, the burden of thinking of menus is reduced, nutritional balance management becomes easier, and time-saving recipes that can be made in under 30 minutes are provided. This system ensures that growing children receive the necessary nutrients while maintaining menu variety. As a result, the lunchbox preparation support system allows parents to prepare lunches efficiently and healthily every day.

[0029] The lunchbox preparation support system according to the embodiment comprises a reception unit, a suggestion unit, a display unit, and a recording unit. The reception unit receives input from the user regarding preferences and allergy information. User preferences include, for example, taste preferences and ingredient preferences, but are not limited to such examples. Allergy information includes, for example, allergies to specific ingredients and the severity of allergies, but are not limited to such examples. The reception unit provides, for example, an interface for the user to access an app and input preferences and allergy information. The reception unit can also support multiple input methods, such as voice input and touch input. The suggestion unit proposes a nutritionally balanced recipe based on the information entered by the reception unit. The suggestion unit generates a recipe that takes into account the user's preferences and allergy information, for example, using AI. The suggestion unit can also analyze the nutrients necessary for growing children and propose menus based on that analysis. For example, the suggestion unit uses AI to generate and propose a nutritionally balanced recipe based on the user's input information. The display unit displays the cooking procedure and ingredient purchase list for the recipe proposed by the suggestion unit. The display unit, for example, displays detailed cooking instructions for a recipe step by step. The display unit can also automatically generate and display a list of ingredients to purchase. For example, it can display a list of the types and quantities of ingredients needed for a recipe, making it easy for the user to purchase them. The recording unit records the bento boxes that have been made. For example, the recording unit can record the bento boxes made by the user with photos, allowing them to check past menu history and nutritional balance. The recording unit can also automatically calculate and record the nutritional information of the bento boxes made by the user. For example, the recording unit can analyze photos of the bento boxes taken by the user, extract nutritional information, and record it. As a result, the bento-making support system according to this embodiment can suggest nutritionally balanced recipes based on the user's preferences and allergy information, display cooking instructions and ingredient purchase lists, and record the bento boxes that have been made.

[0030] The reception desk inputs user preferences and allergy information. User preferences include, but are not limited to, taste preferences and food preferences. Specifically, users can input details through the application, such as whether they like sweets, spicy foods, or specific foods (e.g., chicken, fish, vegetables). Allergy information includes, but is not limited to, allergies to specific foods and the severity of those allergies. Users can input specific allergy information, such as peanut allergies or dairy allergies, and specify the severity (mild, moderate, severe). The reception desk provides an interface for users to access the app and input their preferences and allergy information. The interface is designed to be intuitive and easy to use, allowing users to easily input information. The reception desk can also support multiple input methods, such as voice input and touch input. With voice input, users can input information simply by speaking, and with touch input, they can input information by touching the screen of their smartphone or tablet. This allows the reception desk to meet diverse user needs and efficiently collect information. Furthermore, the reception department can securely store the entered information and utilize it in collaboration with other departments as needed. For example, user preferences and allergy information can be shared with the suggestion and record-keeping departments and used for recipe suggestions and nutritional information recording. This allows the reception department to provide services tailored to the individual needs of users and maximize the overall effectiveness of the system.

[0031] The suggestion department proposes nutritionally balanced recipes based on information entered by the reception department. For example, the suggestion department uses AI to generate recipes that take into account the user's preferences and allergy information. Specifically, the AI ​​analyzes the user's input information and generates the optimal recipe based on past data and nutritional knowledge. For example, if a user likes chicken and has a dairy allergy, the AI ​​will propose a recipe that uses chicken as the main ingredient and does not contain dairy products. The suggestion department can also analyze the nutrients necessary for growing children and propose menus based on that. For example, for children who tend to be deficient in calcium and vitamin D, it will propose recipes using ingredients rich in these nutrients. The suggestion department uses AI to generate and propose nutritionally balanced recipes based on the user's input information. The AI ​​considers the nutritional value of ingredients and cooking methods to generate the optimal recipe based on the user's preferences and allergy information. Furthermore, the suggestion department can also propose recipes that take into account seasonal and regional characteristics. For example, by proposing recipes using seasonal ingredients or recipes that utilize local specialties, it can provide users with new ways to enjoy food. This allows the proposal department to provide a variety of recipes to enhance user health and satisfaction, thereby improving the overall value of the system.

[0032] The display unit shows the cooking procedure and ingredient purchase list for the recipe suggested by the suggestion unit. For example, the display unit displays the detailed cooking procedure of a recipe step by step. Specifically, it displays the necessary ingredients, cooking utensils, and cooking time in detail for each step, allowing the user to proceed with cooking without confusion. The display unit can also automatically generate and display an ingredient purchase list. For example, it can display the types and quantities of ingredients needed for the recipe in a list format, making it easy for the user to purchase them. Furthermore, the display unit can provide information on where to purchase the ingredients and their prices. For example, it can display information on nearby supermarkets and online stores, allowing the user to choose the best place to buy them. To enhance user convenience, the display unit allows the cooking procedure and purchase list to be displayed on multiple devices, such as smartphones, tablets, and PCs. This allows users to easily check the information at home or on the go. The display unit can also accept user feedback and use it to improve the displayed content. For example, users can input comments and ratings on the cooking procedure and purchase list, and the displayed content can be updated based on that feedback. This allows the display unit to respond flexibly to user needs, improving the overall usability of the system.

[0033] The recording function records the lunches that users have prepared. For example, the recording function allows users to record their lunches with photos and check their past menu history and nutritional balance. Specifically, users take photos of their lunches with their smartphones or tablets and upload them to the application. The recording function saves these photos along with the date and menu name, making it easy for users to refer to past menus. The recording function can also automatically calculate and record the nutritional information of the lunches that users have prepared. For example, the recording function analyzes the photos of lunches taken by users, extracts nutritional information, and records it. Using AI-based image analysis technology, it identifies ingredients and their quantities from photos and calculates calorie and nutrient information. Furthermore, the recording function can monitor the user's nutritional balance over the long term and suggest areas for improvement. For example, based on past menu history, if a particular nutrient is deficient, it will suggest recipes that contain more of that nutrient. In this way, the recording function can support users' health management and promote a balanced diet. The recording function also provides a function for users to share menus and recipes they want to share with other users. For example, users can post photos and recipes of their homemade lunches to social media or community sites, allowing them to exchange information with other users. This enables the recording unit to facilitate interaction among users and enhance the overall value of the system.

[0034] The suggestion unit can propose recipes that take nutritional balance into consideration. For example, the suggestion unit can use AI to generate nutritionally balanced recipes based on the user's preferences and allergy information. For example, the suggestion unit can use AI to analyze the user's input information and propose recipes that take into account the balance of calories, vitamins, and minerals. The suggestion unit can also propose recipes that take appropriate nutritional balance into consideration based on the user's health condition and lifestyle. For example, the suggestion unit can propose recipes that contain a lot of specific nutrients based on the health information entered by the user. In this way, by proposing recipes that take nutritional balance into consideration, it can support the creation of healthy lunches. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user information into AI and have the AI ​​generate nutritionally balanced recipes.

[0035] The suggestion unit can analyze the nutrients necessary for growing children and make menu suggestions based on that analysis. For example, the suggestion unit can use AI to analyze the nutrients necessary for growing children. For example, the suggestion unit can have the AI ​​suggest a menu that takes into account nutrients such as protein, calcium, and vitamin D. The suggestion unit can also suggest a menu containing appropriate nutrients based on the child's growth stage and health condition entered by the user. For example, the suggestion unit can have the AI ​​suggest a menu that contains many nutrients necessary for growing children based on the user's input information. In this way, by suggesting a menu that takes into account the nutrients necessary for growing children, it is possible to support the child's growth. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user information into the AI ​​and have the AI ​​generate a menu that takes into account the nutrients necessary for growing children.

[0036] The recording unit can record the bento boxes that have been prepared along with photos, allowing users to check past menu history and nutritional balance. For example, the recording unit can record the bento boxes prepared by the user with photos. For example, the recording unit can save photos of bento boxes taken by the user and record them as part of the past menu history. The recording unit can also automatically calculate and record the nutritional information of the bento boxes prepared by the user. For example, the recording unit can use AI to analyze photos of bento boxes, extract nutritional information, and record it. This allows users to record the bento boxes they have prepared and check past menu history and nutritional balance, thereby maintaining menu variety and managing nutritional balance. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input photos of bento boxes taken by the user into the AI ​​and have the AI ​​extract and record the nutritional information.

[0037] The display unit can display cooking instructions and ingredient purchase lists. For example, the display unit can display the cooking instructions for a recipe suggested by the suggestion unit step by step. For example, the display unit can explain each step of the recipe in detail so that the user can easily cook. The display unit can also automatically generate and display an ingredient purchase list to the user. For example, the display unit can display the types and quantities of ingredients needed for the recipe in a list format so that the user can easily purchase them. This allows the user to efficiently prepare lunch by displaying cooking instructions and ingredient purchase lists. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the recipe information suggested by the suggestion unit into the AI ​​and have the AI ​​generate cooking instructions and ingredient purchase lists.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze the user's past input history. For instance, the reception desk can automatically display preferences and allergy information 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 preferences and allergy information to be used at specific times of the day based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0039] The reception desk can filter user preferences and allergy information based on their current health status and lifestyle. For example, the reception desk can use AI to analyze the user's health status and lifestyle. If the user enters their current health status, the reception desk can filter appropriate preferences and allergy information based on that information. The reception desk can also prioritize displaying relevant information based on the user's lifestyle (e.g., vegetarian, gluten-free). Furthermore, if the user sets a specific health goal (e.g., weight loss, muscle building), the reception desk can suggest preferences and allergy information tailored to that goal. This allows for more appropriate information to be entered by filtering information based on the user's health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user health status and lifestyle data into AI and have the AI ​​perform the filtering.

[0040] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input preferences and allergy information. For example, the reception desk can use AI to analyze the user's geographical location. For instance, if the user lives in a specific region, the reception desk can prioritize inputting allergy information common in that region. Similarly, if the user is traveling, the reception desk can prioritize inputting information about common ingredients and allergies in their travel destination. Furthermore, if the user is interested in the food culture of a particular region, the reception desk can prioritize inputting preferences and allergy information for that region. This allows the system to prioritize inputting highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize inputting highly relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when the user enters their preferences and allergy information. For example, the reception desk can use AI to analyze the user's social media activity. For instance, the reception desk can analyze photos and comments of meals shared by the user on social media and automatically input preferences and allergy information. The reception desk can also suggest relevant preferences and allergy information based on the cooking accounts and groups the user follows. Furthermore, the reception desk can input relevant preferences and allergy information based on the food events and communities the user participates in on social media. This allows for the automatic input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​input relevant information.

[0042] The suggestion function can adjust the level of detail in its suggestions based on the importance of nutritional balance. For example, the suggestion function can use AI to evaluate the importance of nutritional balance. If the user prioritizes nutritional balance, the suggestion function can suggest a recipe that includes detailed nutritional information. If the user prioritizes convenience, the suggestion function can also suggest a recipe that is easy to make but also considers nutritional balance. Furthermore, if the user prioritizes a specific nutrient, the suggestion function can suggest a recipe that contains a high amount of that nutrient. In this way, by adjusting the level of detail in suggestions based on the importance of nutritional balance, the suggestion function can propose recipes that meet the user's needs. Some or all of the above processes in the suggestion function may be performed using AI or not. For example, the suggestion function can input user information into AI and have the AI ​​perform the nutritional balance evaluation and recipe suggestion.

[0043] The suggestion unit can suggest the most suitable recipe by referring to the user's past meal history. For example, the suggestion unit can analyze the user's past meal history using AI. For example, the suggestion unit can suggest similar recipes based on recipes the user has made in the past. The suggestion unit can also suggest new recipes that take nutritional balance into consideration based on the user's past meal history. Furthermore, the suggestion unit can suggest variations of recipes based on recipes the user has enjoyed making in the past. In this way, the optimal recipe can be suggested by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past meal history data into AI and have the AI ​​suggest the optimal recipe.

[0044] The suggestion function can prioritize suggesting recipes that are highly relevant to the user, taking into account the user's geographical location. For example, the suggestion function can use AI to analyze the user's geographical location. For instance, if the user lives in a specific region, the suggestion function can suggest recipes using ingredients common in that region. If the user is traveling, the suggestion function can also suggest recipes using ingredients common in the destination region. Furthermore, if the user is interested in the food culture of a particular region, the suggestion function can suggest recipes from that region. This allows the suggestion function to prioritize suggesting recipes that are highly relevant by considering the user's geographical location. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the user's geographical location into the AI ​​and have the AI ​​suggest highly relevant recipes.

[0045] The suggestion unit can analyze the user's social media activity and suggest relevant recipes when making suggestions. For example, the suggestion unit can use AI to analyze the user's social media activity. For instance, it can analyze photos and comments of meals shared by the user on social media and suggest relevant recipes. It can also suggest relevant recipes based on information about cooking accounts and groups that the user follows. Furthermore, it can suggest relevant recipes based on information about food events and communities that the user participates in on social media. In this way, relevant recipes can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into AI and have the AI ​​suggest relevant recipes.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can analyze the user's past operation history using AI. For example, the display unit can suggest the optimal display method based on the display method the user has preferred to use in the past. The display unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the display unit can suggest the optimal display method based on information about devices the user has used in the past. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0047] The display unit can customize the displayed content based on the user's current lifestyle. For example, the display unit can analyze the user's lifestyle using AI. For instance, if the user is busy, the display unit can provide concise and to-the-point content. If the user is relaxed, the display unit can also provide content that includes detailed information. Furthermore, if the user has set specific health goals, the display unit can provide content tailored to those goals. This allows the display unit to provide information that is appropriate for the user by customizing the displayed content based on the user's current lifestyle. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user lifestyle data into AI and have the AI ​​perform the customization of the displayed content.

[0048] The display unit can select the optimal display method by considering the user's device information when displaying information. For example, the display unit can analyze the user's device information using AI. For instance, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0049] The display unit can analyze the user's social media activity and display relevant information when displaying information. For example, the display unit can use AI to analyze the user's social media activity. For example, the display unit can analyze photos and comments of meals shared by the user on social media and display relevant recipes and nutritional information. The display unit can also display relevant recipes and nutritional information based on information about cooking accounts and groups that the user follows. Furthermore, the display unit can display relevant recipes and nutritional information based on information about food events and communities that the user participates in on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's social media activity data into AI and have the AI ​​perform the display of relevant information.

[0050] The recording unit can suggest the optimal recording method by referring to the user's past meal history during recording. For example, the recording unit can analyze the user's past meal history using AI. For example, the recording unit can suggest the optimal recording method based on the methods the user has used to record meals in the past. The recording unit can also suggest a recording method that takes nutritional balance into account based on the user's past meal history. Furthermore, the recording unit can suggest variations of recording methods based on the recording methods the user has preferred to use in the past. In this way, the optimal recording method can be suggested by referring to the user's past meal history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past meal history data into AI and have the AI ​​suggest the optimal recording method.

[0051] The recording unit can customize the recorded content based on the user's current health status at the time of recording. For example, the recording unit can analyze the user's health status using AI. For example, if the user inputs their current health status, the recording unit can suggest appropriate recorded content based on that information. The recording unit can also prioritize displaying relevant recorded content based on the user's lifestyle (e.g., vegetarian, gluten-free, etc.). Furthermore, if the user sets a specific health goal (e.g., weight loss, muscle building, etc.), the recording unit can suggest recorded content tailored to that goal. In this way, by customizing the recorded content based on the user's current health status, information suitable for the user can be recorded. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's health status data into AI and have the AI ​​perform the customization of the recorded content.

[0052] The recording unit can prioritize recording highly relevant information by considering the user's geographical location during recording. For example, the recording unit can analyze the user's geographical location using AI. For instance, if the user lives in a specific region, the recording unit can prioritize recording information about common foods and nutrition in that region. Furthermore, if the user is traveling, the recording unit can prioritize recording information about common foods and nutrition in the destination region. Additionally, if the user is interested in the food culture of a particular region, the recording unit can prioritize recording information about the local food culture. This allows the recording unit to prioritize recording highly relevant information by considering the user's geographical location. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location into the AI ​​and have the AI ​​record highly relevant information.

[0053] The recording unit can analyze the user's social media activity and record relevant information during recording. For example, the recording unit can use AI to analyze the user's social media activity. For instance, the recording unit can analyze photos and comments of meals shared by the user on social media and record relevant nutritional information. The recording unit can also record relevant nutritional information based on information about cooking accounts and groups that the user follows. Furthermore, the recording unit can record relevant nutritional information based on information about food events and communities that the user participates in on social media. In this way, relevant information can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity data into AI and have the AI ​​record the relevant information.

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

[0055] The bento box preparation support system can also be equipped with a voice assistant. The voice assistant can provide recipe suggestions and generate ingredient shopping lists based on voice commands from the user. For example, if the user says, "Tell me today's bento recipe," the voice assistant will guide the user with an appropriate recipe based on information from the suggestion unit. Also, if the user says, "Create a shopping list," the voice assistant can work in conjunction with the display unit to read aloud a list of the necessary ingredients. Furthermore, the voice assistant can guide the user through the cooking process by voice even when their hands are busy cooking. This allows the user to obtain information without using their hands, improving cooking efficiency.

[0056] The bento box preparation support system can also include a customization feature. This customization feature allows users to tailor recipes and suggestions to their preferences. For example, if a user prefers a specific ingredient, the system can be set to prioritize recipes containing that ingredient. Similarly, if a user prefers a specific cooking method, recipes using that method can be prioritized. Furthermore, if a user prioritizes a particular nutrient, the customization feature can be set to suggest recipes containing that nutrient. This allows users to easily find recipes that suit their preferences, increasing their satisfaction.

[0057] The lunchbox preparation support system can also be equipped with a notification function. This notification function can notify the user of important information and reminders. For example, it can send a reminder to start preparing the lunchbox at a time set by the user. It can also notify the user if ingredients added to their shopping list are on sale at a specific store. Furthermore, if the nutritional balance of a lunchbox the user has made in the past is unbalanced, the notification function can send a notification suggesting ways to improve it. This allows the user to prepare lunchboxes efficiently without missing important information.

[0058] The bento-making support system can also include a sharing section. This section allows users to share recipes and photos of bento boxes they have made with other users. For example, users can upload photos of their bento boxes to the sharing section, and other users can view, comment on, and rate those photos. The sharing section can also be used to share recipes created by users, increasing the variety of recipes available. Furthermore, the sharing section can be used by users to host bento-making contests based on specific themes, allowing them to compete with other users. This enables users to share information with others and form communities.

[0059] The bento-making support system can also include a learning section. This section can provide content for users to learn new cooking techniques and recipes. For example, it could offer cooking technique tutorials in video or text format to support users in acquiring new skills. The learning section could also provide information for users to learn how to use and store specific ingredients. Furthermore, it could provide content for users to learn about nutrition and balanced meals. This allows users to improve their bento-making skills and create healthier and more delicious lunches.

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

[0061] Step 1: The reception desk inputs the user's preferences and allergy information. User preferences include, but are not limited to, taste preferences and food preferences. Allergy information includes, but are not limited to, allergies to specific foods and the severity of allergies. The reception desk provides an interface for the user to access the app and input their preferences and allergy information. It can also support multiple input methods, such as voice input and touch input. Step 2: The suggestion department proposes nutritionally balanced recipes based on the information entered by the reception department. The suggestion department uses AI to generate recipes that take into account the user's preferences and allergy information. It can also analyze the nutrients necessary for growing children and propose menus based on that analysis. Step 3: The display unit shows the cooking procedure and ingredient purchase list for the recipe suggested by the suggestion unit. The display unit displays the detailed cooking procedure of the recipe step by step and automatically generates and displays an ingredient purchase list to the user. For example, it displays the types and quantities of ingredients required for the recipe in a list format, making it easy for the user to purchase them. Step 4: The recording unit records the lunch boxes that have been prepared. The recording unit records the lunch boxes prepared by the user with photos, and allows users to check past menu history and nutritional balance. It can also automatically calculate and record the nutritional information of the lunch boxes prepared by the user. For example, it can analyze photos of lunch boxes taken by the user, extract nutritional information, and record it.

[0062] (Example of form 2) The lunchbox preparation support system according to an embodiment of the present invention is an integrated solution that supports parents who struggle with preparing lunchboxes every day. This lunchbox preparation support system solves challenges faced by parents, such as "lack of menu ideas," "managing nutritional balance," and "wanting to prepare lunches quickly during busy mornings," using an app equipped with AI functionality. This function proposes lunchbox menus every day that take into account nutritional balance, ease of preparation, and visual appeal, and automatically generates a weekly menu plan and shopping list. In addition, it allows users to record the lunchboxes they have made with photos and check past menu history and nutritional balance. Furthermore, it analyzes the nutrients necessary for growing children and proposes menus based on that. For example, a user accesses the app and enters their preferences and allergy information. Next, the AI ​​proposes a nutritionally balanced recipe based on this information. The proposed recipe is displayed along with cooking instructions and a list of ingredients to purchase. Furthermore, users can record the lunchboxes they have made with photos and check past menu history and nutritional balance. As a result, the burden of thinking of menus is reduced, nutritional balance management becomes easier, and time-saving recipes that can be made in under 30 minutes are provided. This system ensures that growing children receive the necessary nutrients while maintaining menu variety. As a result, the lunchbox preparation support system allows parents to prepare lunches efficiently and healthily every day.

[0063] The lunchbox preparation support system according to the embodiment comprises a reception unit, a suggestion unit, a display unit, and a recording unit. The reception unit receives input from the user regarding preferences and allergy information. User preferences include, for example, taste preferences and ingredient preferences, but are not limited to such examples. Allergy information includes, for example, allergies to specific ingredients and the severity of allergies, but are not limited to such examples. The reception unit provides, for example, an interface for the user to access an app and input preferences and allergy information. The reception unit can also support multiple input methods, such as voice input and touch input. The suggestion unit proposes a nutritionally balanced recipe based on the information entered by the reception unit. The suggestion unit generates a recipe that takes into account the user's preferences and allergy information, for example, using AI. The suggestion unit can also analyze the nutrients necessary for growing children and propose menus based on that analysis. For example, the suggestion unit uses AI to generate and propose a nutritionally balanced recipe based on the user's input information. The display unit displays the cooking procedure and ingredient purchase list for the recipe proposed by the suggestion unit. The display unit, for example, displays detailed cooking instructions for a recipe step by step. The display unit can also automatically generate and display a list of ingredients to purchase. For example, it can display a list of the types and quantities of ingredients needed for a recipe, making it easy for the user to purchase them. The recording unit records the bento boxes that have been made. For example, the recording unit can record the bento boxes made by the user with photos, allowing them to check past menu history and nutritional balance. The recording unit can also automatically calculate and record the nutritional information of the bento boxes made by the user. For example, the recording unit can analyze photos of the bento boxes taken by the user, extract nutritional information, and record it. As a result, the bento-making support system according to this embodiment can suggest nutritionally balanced recipes based on the user's preferences and allergy information, display cooking instructions and ingredient purchase lists, and record the bento boxes that have been made.

[0064] The reception desk inputs user preferences and allergy information. User preferences include, but are not limited to, taste preferences and food preferences. Specifically, users can input details through the application, such as whether they like sweets, spicy foods, or specific foods (e.g., chicken, fish, vegetables). Allergy information includes, but is not limited to, allergies to specific foods and the severity of those allergies. Users can input specific allergy information, such as peanut allergies or dairy allergies, and specify the severity (mild, moderate, severe). The reception desk provides an interface for users to access the app and input their preferences and allergy information. The interface is designed to be intuitive and easy to use, allowing users to easily input information. The reception desk can also support multiple input methods, such as voice input and touch input. With voice input, users can input information simply by speaking, and with touch input, they can input information by touching the screen of their smartphone or tablet. This allows the reception desk to meet diverse user needs and efficiently collect information. Furthermore, the reception department can securely store the entered information and utilize it in collaboration with other departments as needed. For example, user preferences and allergy information can be shared with the suggestion and record-keeping departments and used for recipe suggestions and nutritional information recording. This allows the reception department to provide services tailored to the individual needs of users and maximize the overall effectiveness of the system.

[0065] The suggestion department proposes nutritionally balanced recipes based on information entered by the reception department. For example, the suggestion department uses AI to generate recipes that take into account the user's preferences and allergy information. Specifically, the AI ​​analyzes the user's input information and generates the optimal recipe based on past data and nutritional knowledge. For example, if a user likes chicken and has a dairy allergy, the AI ​​will propose a recipe that uses chicken as the main ingredient and does not contain dairy products. The suggestion department can also analyze the nutrients necessary for growing children and propose menus based on that. For example, for children who tend to be deficient in calcium and vitamin D, it will propose recipes using ingredients rich in these nutrients. The suggestion department uses AI to generate and propose nutritionally balanced recipes based on the user's input information. The AI ​​considers the nutritional value of ingredients and cooking methods to generate the optimal recipe based on the user's preferences and allergy information. Furthermore, the suggestion department can also propose recipes that take into account seasonal and regional characteristics. For example, by proposing recipes using seasonal ingredients or recipes that utilize local specialties, it can provide users with new ways to enjoy food. This allows the proposal department to provide a variety of recipes to enhance user health and satisfaction, thereby improving the overall value of the system.

[0066] The display unit shows the cooking procedure and ingredient purchase list for the recipe suggested by the suggestion unit. For example, the display unit displays the detailed cooking procedure of a recipe step by step. Specifically, it displays the necessary ingredients, cooking utensils, and cooking time in detail for each step, allowing the user to proceed with cooking without confusion. The display unit can also automatically generate and display an ingredient purchase list. For example, it can display the types and quantities of ingredients needed for the recipe in a list format, making it easy for the user to purchase them. Furthermore, the display unit can provide information on where to purchase the ingredients and their prices. For example, it can display information on nearby supermarkets and online stores, allowing the user to choose the best place to buy them. To enhance user convenience, the display unit allows the cooking procedure and purchase list to be displayed on multiple devices, such as smartphones, tablets, and PCs. This allows users to easily check the information at home or on the go. The display unit can also accept user feedback and use it to improve the displayed content. For example, users can input comments and ratings on the cooking procedure and purchase list, and the displayed content can be updated based on that feedback. This allows the display unit to respond flexibly to user needs, improving the overall usability of the system.

[0067] The recording function records the lunches that users have prepared. For example, the recording function allows users to record their lunches with photos and check their past menu history and nutritional balance. Specifically, users take photos of their lunches with their smartphones or tablets and upload them to the application. The recording function saves these photos along with the date and menu name, making it easy for users to refer to past menus. The recording function can also automatically calculate and record the nutritional information of the lunches that users have prepared. For example, the recording function analyzes the photos of lunches taken by users, extracts nutritional information, and records it. Using AI-based image analysis technology, it identifies ingredients and their quantities from photos and calculates calorie and nutrient information. Furthermore, the recording function can monitor the user's nutritional balance over the long term and suggest areas for improvement. For example, based on past menu history, if a particular nutrient is deficient, it will suggest recipes that contain more of that nutrient. In this way, the recording function can support users' health management and promote a balanced diet. The recording function also provides a function for users to share menus and recipes they want to share with other users. For example, users can post photos and recipes of their homemade lunches to social media or community sites, allowing them to exchange information with other users. This enables the recording unit to facilitate interaction among users and enhance the overall value of the system.

[0068] The suggestion unit can propose recipes that take nutritional balance into consideration. For example, the suggestion unit can use AI to generate nutritionally balanced recipes based on the user's preferences and allergy information. For example, the suggestion unit can use AI to analyze the user's input information and propose recipes that take into account the balance of calories, vitamins, and minerals. The suggestion unit can also propose recipes that take appropriate nutritional balance into consideration based on the user's health condition and lifestyle. For example, the suggestion unit can propose recipes that contain a lot of specific nutrients based on the health information entered by the user. In this way, by proposing recipes that take nutritional balance into consideration, it can support the creation of healthy lunches. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user information into AI and have the AI ​​generate nutritionally balanced recipes.

[0069] The suggestion unit can analyze the nutrients necessary for growing children and make menu suggestions based on that analysis. For example, the suggestion unit can use AI to analyze the nutrients necessary for growing children. For example, the suggestion unit can have the AI ​​suggest a menu that takes into account nutrients such as protein, calcium, and vitamin D. The suggestion unit can also suggest a menu containing appropriate nutrients based on the child's growth stage and health condition entered by the user. For example, the suggestion unit can have the AI ​​suggest a menu that contains many nutrients necessary for growing children based on the user's input information. In this way, by suggesting a menu that takes into account the nutrients necessary for growing children, it is possible to support the child's growth. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user information into the AI ​​and have the AI ​​generate a menu that takes into account the nutrients necessary for growing children.

[0070] The recording unit can record the bento boxes that have been prepared along with photos, allowing users to check past menu history and nutritional balance. For example, the recording unit can record the bento boxes prepared by the user with photos. For example, the recording unit can save photos of bento boxes taken by the user and record them as part of the past menu history. The recording unit can also automatically calculate and record the nutritional information of the bento boxes prepared by the user. For example, the recording unit can use AI to analyze photos of bento boxes, extract nutritional information, and record it. This allows users to record the bento boxes they have prepared and check past menu history and nutritional balance, thereby maintaining menu variety and managing nutritional balance. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input photos of bento boxes taken by the user into the AI ​​and have the AI ​​extract and record the nutritional information.

[0071] The display unit can display cooking instructions and ingredient purchase lists. For example, the display unit can display the cooking instructions for a recipe suggested by the suggestion unit step by step. For example, the display unit can explain each step of the recipe in detail so that the user can easily cook. The display unit can also automatically generate and display an ingredient purchase list to the user. For example, the display unit can display the types and quantities of ingredients needed for the recipe in a list format so that the user can easily purchase them. This allows the user to efficiently prepare lunch by displaying cooking instructions and ingredient purchase lists. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the recipe information suggested by the suggestion unit into the AI ​​and have the AI ​​generate cooking instructions and ingredient purchase lists.

[0072] The reception desk can estimate the user's emotions and adjust the input method for preferences and allergy information based on the estimated emotions. The reception desk estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk analyzes the user's facial expressions and voice to estimate emotions. Furthermore, the reception desk can adjust the input method for preferences and allergy information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Additionally, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of preferences and allergy information. This allows users to input information without stress by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, or they may not be performed using AI. For example, the reception desk can input the user's facial expressions and voice data into a generating AI and have the generating AI perform emotion estimation.

[0073] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze the user's past input history. For instance, the reception desk can automatically display preferences and allergy information 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 preferences and allergy information to be used at specific times of the day based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0074] The reception desk can filter user preferences and allergy information based on their current health status and lifestyle. For example, the reception desk can use AI to analyze the user's health status and lifestyle. If the user enters their current health status, the reception desk can filter appropriate preferences and allergy information based on that information. The reception desk can also prioritize displaying relevant information based on the user's lifestyle (e.g., vegetarian, gluten-free). Furthermore, if the user sets a specific health goal (e.g., weight loss, muscle building), the reception desk can suggest preferences and allergy information tailored to that goal. This allows for more appropriate information to be entered by filtering information based on the user's health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user health status and lifestyle data into AI and have the AI ​​perform the filtering.

[0075] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. The reception desk estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk analyzes the user's facial expressions and voice to estimate emotions. Furthermore, the reception desk can prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of the most important information (such as allergy information). If the user is relaxed, the reception desk may also prioritize the input of detailed preference information. Additionally, if the user is in a hurry, the reception desk may prioritize the input of minimal information (such as major allergy information). This allows for the priority of important information to be entered by determining the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, or they may not be performed using AI. For example, the reception desk can input the user's facial expressions and voice data into a generating AI and have the generating AI perform emotion estimation.

[0076] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input preferences and allergy information. For example, the reception desk can use AI to analyze the user's geographical location. For instance, if the user lives in a specific region, the reception desk can prioritize inputting allergy information common in that region. Similarly, if the user is traveling, the reception desk can prioritize inputting information about common ingredients and allergies in their travel destination. Furthermore, if the user is interested in the food culture of a particular region, the reception desk can prioritize inputting preferences and allergy information for that region. This allows the system to prioritize inputting highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize inputting highly relevant information.

[0077] The reception desk can analyze the user's social media activity and input relevant information when the user enters their preferences and allergy information. For example, the reception desk can use AI to analyze the user's social media activity. For instance, the reception desk can analyze photos and comments of meals shared by the user on social media and automatically input preferences and allergy information. The reception desk can also suggest relevant preferences and allergy information based on the cooking accounts and groups the user follows. Furthermore, the reception desk can input relevant preferences and allergy information based on the food events and communities the user participates in on social media. This allows for the automatic input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​input relevant information.

[0078] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated emotions. The suggestion unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the suggestion unit analyzes the user's facial expressions and voice to estimate emotions. The suggestion unit can also adjust the recipe suggestion method based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will prioritize suggesting easy and quick recipes. If the user is relaxed, the suggestion unit can also suggest recipes that can be enjoyed over time. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting time-saving recipes. In this way, by adjusting the recipe suggestion method according to the user's emotions, recipes suitable for the user can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal unit can input the user's facial expressions and voice data into a generating AI and have the AI ​​perform emotion estimation.

[0079] The suggestion function can adjust the level of detail in its suggestions based on the importance of nutritional balance. For example, the suggestion function can use AI to evaluate the importance of nutritional balance. If the user prioritizes nutritional balance, the suggestion function can suggest a recipe that includes detailed nutritional information. If the user prioritizes convenience, the suggestion function can also suggest a recipe that is easy to make but also considers nutritional balance. Furthermore, if the user prioritizes a specific nutrient, the suggestion function can suggest a recipe that contains a high amount of that nutrient. In this way, by adjusting the level of detail in suggestions based on the importance of nutritional balance, the suggestion function can propose recipes that meet the user's needs. Some or all of the above processes in the suggestion function may be performed using AI or not. For example, the suggestion function can input user information into AI and have the AI ​​perform the nutritional balance evaluation and recipe suggestion.

[0080] The suggestion unit can suggest the most suitable recipe by referring to the user's past meal history. For example, the suggestion unit can analyze the user's past meal history using AI. For example, the suggestion unit can suggest similar recipes based on recipes the user has made in the past. The suggestion unit can also suggest new recipes that take nutritional balance into consideration based on the user's past meal history. Furthermore, the suggestion unit can suggest variations of recipes based on recipes the user has enjoyed making in the past. In this way, the optimal recipe can be suggested by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past meal history data into AI and have the AI ​​suggest the optimal recipe.

[0081] The suggestion unit can estimate the user's emotions and determine the priority of suggested recipes based on the estimated emotions. The suggestion unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the suggestion unit analyzes the user's facial expressions and voice to estimate emotions. The suggestion unit can also determine the priority of suggested recipes based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting easy and quick recipes. If the user is relaxed, the suggestion unit may also prioritize suggesting recipes that can be enjoyed over time. Furthermore, if the user is in a hurry, the suggestion unit may also prioritize suggesting time-saving recipes. In this way, by determining the priority of recipes according to the user's emotions, the system can prioritize suggesting recipes that are suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal unit can input the user's facial expressions and voice data into a generating AI and have the AI ​​perform emotion estimation.

[0082] The suggestion function can prioritize suggesting recipes that are highly relevant to the user, taking into account the user's geographical location. For example, the suggestion function can use AI to analyze the user's geographical location. For instance, if the user lives in a specific region, the suggestion function can suggest recipes using ingredients common in that region. If the user is traveling, the suggestion function can also suggest recipes using ingredients common in the destination region. Furthermore, if the user is interested in the food culture of a particular region, the suggestion function can suggest recipes from that region. This allows the suggestion function to prioritize suggesting recipes that are highly relevant by considering the user's geographical location. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the user's geographical location into the AI ​​and have the AI ​​suggest highly relevant recipes.

[0083] The suggestion unit can analyze the user's social media activity and suggest relevant recipes when making suggestions. For example, the suggestion unit can use AI to analyze the user's social media activity. For instance, it can analyze photos and comments of meals shared by the user on social media and suggest relevant recipes. It can also suggest relevant recipes based on information about cooking accounts and groups that the user follows. Furthermore, it can suggest relevant recipes based on information about food events and communities that the user participates in on social media. In this way, relevant recipes can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into AI and have the AI ​​suggest relevant recipes.

[0084] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. The display unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, the display unit analyzes the user's facial expressions and voice to estimate emotions. The display unit can also adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit can provide an interface with calm colors to reduce visual stress. If the user is enjoying themselves, the display unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the display unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the display method according to the user's emotions, a display method suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's facial expressions and voice data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0085] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can analyze the user's past operation history using AI. For example, the display unit can suggest the optimal display method based on the display method the user has preferred to use in the past. The display unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the display unit can suggest the optimal display method based on information about devices the user has used in the past. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0086] The display unit can customize the displayed content based on the user's current lifestyle. For example, the display unit can analyze the user's lifestyle using AI. For instance, if the user is busy, the display unit can provide concise and to-the-point content. If the user is relaxed, the display unit can also provide content that includes detailed information. Furthermore, if the user has set specific health goals, the display unit can provide content tailored to those goals. This allows the display unit to provide information that is appropriate for the user by customizing the displayed content based on the user's current lifestyle. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user lifestyle data into AI and have the AI ​​perform the customization of the displayed content.

[0087] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated emotions. The display unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the display unit analyzes the user's facial expressions and voice to estimate emotions. The display unit can also determine the priority of information to display based on the estimated emotions. For example, if the user is tense, the display unit will prioritize displaying the most important information. The display unit can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can prioritize displaying concise information. In this way, by determining the priority of information to display according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's facial expressions and voice data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0088] The display unit can select the optimal display method by considering the user's device information when displaying information. For example, the display unit can analyze the user's device information using AI. For instance, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0089] The display unit can analyze the user's social media activity and display relevant information when displaying information. For example, the display unit can use AI to analyze the user's social media activity. For example, the display unit can analyze photos and comments of meals shared by the user on social media and display relevant recipes and nutritional information. The display unit can also display relevant recipes and nutritional information based on information about cooking accounts and groups that the user follows. Furthermore, the display unit can display relevant recipes and nutritional information based on information about food events and communities that the user participates in on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's social media activity data into AI and have the AI ​​perform the display of relevant information.

[0090] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. The recording unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the recording unit analyzes the user's facial expressions and voice to estimate emotions. Furthermore, the recording unit can adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can provide an interface that allows for easy recording. If the user is relaxed, the recording unit can also provide detailed recording options and suggest a customizable recording method. Additionally, if the user is in a hurry, the recording unit can prioritize voice input to enable quick recording. This allows the recording unit to provide a recording method suitable for the user by adjusting the recording method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recording unit may be performed using AI or not. For example, the recording unit can input the user's facial expressions and voice data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0091] The recording unit can suggest the optimal recording method by referring to the user's past meal history during recording. For example, the recording unit can analyze the user's past meal history using AI. For example, the recording unit can suggest the optimal recording method based on the methods the user has used to record meals in the past. The recording unit can also suggest a recording method that takes nutritional balance into account based on the user's past meal history. Furthermore, the recording unit can suggest variations of recording methods based on the recording methods the user has preferred to use in the past. In this way, the optimal recording method can be suggested by referring to the user's past meal history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past meal history data into AI and have the AI ​​suggest the optimal recording method.

[0092] The recording unit can customize the recorded content based on the user's current health status at the time of recording. For example, the recording unit can analyze the user's health status using AI. For example, if the user inputs their current health status, the recording unit can suggest appropriate recorded content based on that information. The recording unit can also prioritize displaying relevant recorded content based on the user's lifestyle (e.g., vegetarian, gluten-free, etc.). Furthermore, if the user sets a specific health goal (e.g., weight loss, muscle building, etc.), the recording unit can suggest recorded content tailored to that goal. In this way, by customizing the recorded content based on the user's current health status, information suitable for the user can be recorded. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's health status data into AI and have the AI ​​perform the customization of the recorded content.

[0093] The recording unit can estimate the user's emotions and determine the priority of information to record based on the estimated emotions. The recording unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the recording unit analyzes the user's facial expressions and voice to estimate emotions. The recording unit can also determine the priority of information to record based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording the most important information (e.g., allergy information). If the user is relaxed, the recording unit may also prioritize recording detailed information. Furthermore, if the user is in a hurry, the recording unit may prioritize recording minimal information (e.g., essential nutrients). In this way, by determining the priority of information to record according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's facial expressions and voice data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0094] The recording unit can prioritize recording highly relevant information by considering the user's geographical location during recording. For example, the recording unit can analyze the user's geographical location using AI. For instance, if the user lives in a specific region, the recording unit can prioritize recording information about common foods and nutrition in that region. Furthermore, if the user is traveling, the recording unit can prioritize recording information about common foods and nutrition in the destination region. Additionally, if the user is interested in the food culture of a particular region, the recording unit can prioritize recording information about the local food culture. This allows the recording unit to prioritize recording highly relevant information by considering the user's geographical location. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location into the AI ​​and have the AI ​​record highly relevant information.

[0095] The recording unit can analyze the user's social media activity and record relevant information during recording. For example, the recording unit can use AI to analyze the user's social media activity. For instance, the recording unit can analyze photos and comments of meals shared by the user on social media and record relevant nutritional information. The recording unit can also record relevant nutritional information based on information about cooking accounts and groups that the user follows. Furthermore, the recording unit can record relevant nutritional information based on information about food events and communities that the user participates in on social media. In this way, relevant information can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity data into AI and have the AI ​​record the relevant information.

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

[0097] The bento box preparation support system can also be equipped with a voice assistant. The voice assistant can provide recipe suggestions and generate ingredient shopping lists based on voice commands from the user. For example, if the user says, "Tell me today's bento recipe," the voice assistant will guide the user with an appropriate recipe based on information from the suggestion unit. Also, if the user says, "Create a shopping list," the voice assistant can work in conjunction with the display unit to read aloud a list of the necessary ingredients. Furthermore, the voice assistant can guide the user through the cooking process by voice even when their hands are busy cooking. This allows the user to obtain information without using their hands, improving cooking efficiency.

[0098] The bento box preparation support system can also include a customization feature. This customization feature allows users to tailor recipes and suggestions to their preferences. For example, if a user prefers a specific ingredient, the system can be set to prioritize recipes containing that ingredient. Similarly, if a user prefers a specific cooking method, recipes using that method can be prioritized. Furthermore, if a user prioritizes a particular nutrient, the customization feature can be set to suggest recipes containing that nutrient. This allows users to easily find recipes that suit their preferences, increasing their satisfaction.

[0099] The lunchbox preparation support system can also be equipped with a notification function. This notification function can notify the user of important information and reminders. For example, it can send a reminder to start preparing the lunchbox at a time set by the user. It can also notify the user if ingredients added to their shopping list are on sale at a specific store. Furthermore, if the nutritional balance of a lunchbox the user has made in the past is unbalanced, the notification function can send a notification suggesting ways to improve it. This allows the user to prepare lunchboxes efficiently without missing important information.

[0100] The bento-making support system can also include a sharing section. This section allows users to share recipes and photos of bento boxes they have made with other users. For example, users can upload photos of their bento boxes to the sharing section, and other users can view, comment on, and rate those photos. The sharing section can also be used to share recipes created by users, increasing the variety of recipes available. Furthermore, the sharing section can be used by users to host bento-making contests based on specific themes, allowing them to compete with other users. This enables users to share information with others and form communities.

[0101] The bento-making support system can also include a learning section. This section can provide content for users to learn new cooking techniques and recipes. For example, it could offer cooking technique tutorials in video or text format to support users in acquiring new skills. The learning section could also provide information for users to learn how to use and store specific ingredients. Furthermore, it could provide content for users to learn about nutrition and balanced meals. This allows users to improve their bento-making skills and create healthier and more delicious lunches.

[0102] The bento-making support system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated emotions. For example, the emotion estimation unit can analyze the user's facial expressions and voice, and if the user is feeling stressed, it will prioritize suggesting easy and quick recipes. If the user is relaxed, the emotion estimation unit can also suggest recipes that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize suggesting time-saving recipes. In this way, by adjusting the recipe suggestion method according to the user's emotions, the system can suggest recipes that are suitable for the user.

[0103] The bento-making support system can also be equipped with an emotional feedback unit. This unit can record the user's feelings about the bento they've made and adjust future suggestions based on those feelings. For example, the emotional feedback unit provides an interface for the user to input feelings about the bento they made, such as "It was delicious" or "It was a lot of work." Furthermore, the emotional feedback unit can analyze the user's input and reflect it in future recipe suggestions. Additionally, if the user has positive feelings towards a particular recipe, the emotional feedback unit can suggest that recipe again. This allows for the optimization of recipe suggestions based on the user's feelings, thereby improving satisfaction.

[0104] The bento-making support system can also be equipped with an emotional history function. This function can record the user's past emotional history and suggest recipes based on that history. For example, the emotional history function can record the user's feelings about bento boxes they have made in the past and analyze that history. It can also prioritize suggesting recipes that the user has had positive feelings about in the past. Furthermore, it can suggest avoiding recipes that the user has had negative feelings about in the past. This optimizes recipe suggestions based on the user's past emotional history and improves user satisfaction.

[0105] The lunchbox preparation support system can also be equipped with an emotion analysis unit. This unit can analyze the user's emotions in real time and adjust the overall system operation based on the results. For example, it can analyze the user's facial expressions and voice while they are viewing recipes to determine if they are interested. If the user is interested, the unit can suggest other recipes related to that one. Furthermore, if the user is feeling stressed, the unit can simplify the system interface and make it easier to operate. This allows the system to adjust its operation according to the user's emotions, improving the user experience.

[0106] The bento-making support system can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past behavior and input data, and then suggest recipes based on those predictions. For example, the emotion prediction unit can analyze how a user felt in specific situations in the past and predict those emotions when similar situations occur. Furthermore, if the emotion prediction unit tends to have positive feelings towards certain ingredients or cooking methods, it can suggest recipes using those ingredients or methods. Additionally, if the emotion prediction unit is prone to feeling stressed during certain times of the day, it can suggest easy and convenient recipes for those times. By predicting the user's future emotions and suggesting appropriate recipes, the system can improve user satisfaction.

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

[0108] Step 1: The reception desk inputs the user's preferences and allergy information. User preferences include, but are not limited to, taste preferences and food preferences. Allergy information includes, but are not limited to, allergies to specific foods and the severity of allergies. The reception desk provides an interface for the user to access the app and input their preferences and allergy information. It can also support multiple input methods, such as voice input and touch input. Step 2: The suggestion department proposes nutritionally balanced recipes based on the information entered by the reception department. The suggestion department uses AI to generate recipes that take into account the user's preferences and allergy information. It can also analyze the nutrients necessary for growing children and propose menus based on that analysis. Step 3: The display unit shows the cooking procedure and ingredient purchase list for the recipe suggested by the suggestion unit. The display unit displays the detailed cooking procedure of the recipe step by step and automatically generates and displays an ingredient purchase list to the user. For example, it displays the types and quantities of ingredients required for the recipe in a list format, making it easy for the user to purchase them. Step 4: The recording unit records the lunch boxes that have been prepared. The recording unit records the lunch boxes prepared by the user with photos, and allows users to check past menu history and nutritional balance. It can also automatically calculate and record the nutritional information of the lunch boxes prepared by the user. For example, it can analyze photos of lunch boxes taken by the user, extract nutritional information, and record it.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, suggestion unit, display unit, and recording unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for inputting user preferences and allergy information. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate and suggest nutritionally balanced recipes. The display unit is implemented by the output device 40 of the smart device 14 and displays the cooking procedure and ingredient purchase list for the suggested recipe. The recording unit is implemented by the camera 42 of the smart device 14 and the identification processing unit 290 of the data processing unit 12 and records the prepared lunch box with a photograph, and checks past menu history and nutritional balance. 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.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, suggestion unit, display unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for inputting user preferences and allergy information. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate and suggest nutritionally balanced recipes. The display unit is implemented by the speaker 240 of the smart glasses 214 and displays the cooking procedure and ingredient purchase list for the suggested recipe. The recording unit is implemented by the camera 42 of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12 and records the prepared lunchbox with a photograph, and checks past menu history and nutritional balance. 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.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, suggestion unit, display unit, and recording unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for inputting user preferences and allergy information. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to generate and suggest nutritionally balanced recipes. The display unit is implemented by the display 343 of the headset terminal 314 and displays the cooking procedure and ingredient purchase list for the suggested recipe. The recording unit is implemented by the camera 42 of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12 and records the prepared lunch box with a photograph, and checks past menu history and nutritional balance. 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.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, suggestion unit, display unit, and recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for inputting user preferences and allergy information. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to generate and suggest nutritionally balanced recipes. The display unit is implemented by the speaker 240 of the robot 414 and displays the cooking procedure and ingredient purchase list for the suggested recipe. The recording unit is implemented by the camera 42 of the robot 414 and the identification processing unit 290 of the data processing unit 12 and records the prepared lunch box with a photograph, and checks past menu history and nutritional balance. 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.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception area where users input their preferences and allergy information, Based on the information entered by the reception unit, the proposal unit proposes a nutritionally balanced recipe. A display unit that displays the cooking procedure and ingredient purchase list for the recipe proposed by the aforementioned proposal unit, It includes a recording unit for recording the lunch boxes that have been made. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose recipes that take nutritional balance into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze the nutrients necessary for growing children and propose menus based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recording unit is The system allows users to record their homemade lunches with photos, and to review past menu history and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Display cooking instructions and a list of ingredients to purchase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how preferences and allergy information are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their preferences and allergy information, the system filters the results based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter preferences and allergy information, the system prioritizes inputting more relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their preferences and allergy information, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the recipe suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, the system refers to the user's past meal history to propose the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and prioritizes the suggested recipes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, the system prioritizes suggesting recipes that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and suggest relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displayed, the content is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is It estimates the user's emotions and determines the priority of the information to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is When recording, the system refers to the user's past meal history to suggest the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is During recording, the recording content is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is It estimates the user's emotions and determines the priority of information to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recording unit is During recording, the system prioritizes recording highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recording unit is During recording, the system analyzes the user's social media activity and records relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their preferences and allergy information, Based on the information entered by the reception unit, the proposal unit proposes a nutritionally balanced recipe. A display unit that displays the cooking procedure and ingredient purchase list for the recipe proposed by the aforementioned proposal unit, It includes a recording unit for recording the lunch boxes that have been made. A system characterized by the following features.

2. The aforementioned proposal section is, We propose recipes that take nutritional balance into consideration. The system according to feature 1.

3. The aforementioned proposal section is, We analyze the nutrients necessary for growing children and propose menus based on that analysis. The system according to feature 1.

4. The aforementioned recording unit is The system allows users to record their homemade lunches with photos, and to review past menu history and nutritional balance. The system according to feature 1.

5. The aforementioned display unit is Display cooking instructions and a list of ingredients to purchase. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how preferences and allergy information are entered based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned reception unit is When users enter their preferences and allergy information, the system filters the results based on their current health status and lifestyle. The system according to feature 1.

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