system

Abstract

The system according to the embodiment aims to automatically generate daily menus. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information. The analysis unit analyzes the information received by the reception unit. The generation unit generates a menu based on the information analyzed by the analysis unit. The provision unit provides the menu generated by the generation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem of taking time and effort to plan daily menus.

[0005] The system according to the embodiment aims to automatically generate daily menus. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information. The analysis unit analyzes the information received by the reception unit. The generation unit generates a menu based on the information analyzed by the analysis unit. The provision unit provides the menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate daily menus. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A menu generation system according to an embodiment of the present invention generates and provides an optimal menu based on information input by a user. In the menu generation system, the user inputs information such as ingredients, preferences, and allergy information, and AI analyzes this information to generate an optimal menu. The generated menu takes into account the user's preferences and nutritional balance, enriching daily meals. For example, a user inputs information such as ingredients, preferences, and allergy information. The user inputs information such as the ingredients they own, their favorite dishes, and whether or not they have any allergies. For example, the user inputs information such as "I have chicken, tomatoes, and broccoli," "I like Japanese food," and "I have a nut allergy." This information is input into AI. The AI ​​then analyzes the input information and generates an optimal menu for the user. Based on the information input by the user, the AI ​​generates a menu taking into account nutritional balance, cooking time, seasonal ingredients, and other factors. For example, a menu such as "chicken and tomato stew" or "broccoli salad" may be generated. The generated menu takes into account the user's preferences and nutritional balance. For example, a user who likes Japanese food may be suggested a menu centered on Japanese food, while a user with a nut allergy may be suggested a menu that does not include nuts. In addition, the main dish, side dish, soup, etc. are combined in a balanced manner, taking nutritional balance into consideration. This system allows users to enrich their daily meals. For example, on busy days, easy-to-make menus are suggested, and on days when you have time, elaborate dishes are suggested. In addition, menus using seasonal ingredients are suggested, allowing you to enjoy the feeling of the seasons. Furthermore, menus that take nutritional balance into consideration are suggested, allowing you to eat a healthy diet. In this way, the menu generation system can generate and provide optimal menus based on user information. For example, by generating menus that take nutritional balance, cooking time, seasonal ingredients, etc. into consideration based on information entered by the user, it is possible to enrich the user's meals and support a healthy diet.

[0029] A menu generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information input by a user. The information input by the user includes, but is not limited to, ingredients, preferences, and allergy information. The reception unit allows the user to input information using, for example, a smartphone app. The reception unit can also support multiple input methods, such as voice input, text input, and image input. The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using, for example, a machine learning algorithm or data mining technology, but is not limited to, examples. For example, the analysis unit performs analysis based on the information input by the user, taking into account nutritional balance, cooking time, seasonal ingredients, and the like. The generation unit generates a menu based on the information analyzed by the analysis unit. The generation is performed, for example, taking into account nutritional balance, cooking time, seasonal ingredients, and the like, but is not limited to, examples. For example, the generation unit can generate a menu taking into account the user's preferences and allergy information. The provision unit provides the menu generated by the generation unit. The provision of the information may be, for example, by notifying the user on their smartphone or by displaying the information within an app, but is not limited to these examples. For example, the provision unit may notify the user that the generated menu takes into consideration the user's preferences and nutritional balance. This allows the menu generation system according to the embodiment to generate and provide an optimal menu based on user information. For example, by generating a menu that takes into consideration nutritional balance, cooking time, seasonal ingredients, etc. based on information input by the user, the system can enrich the user's meals and support a healthy eating lifestyle.

[0030] The reception unit allows a user to input information using a smartphone app. The smartphone app includes functions such as, but not limited to, inputting ingredients, preferences, and allergy information. For example, the reception unit allows a user to input ingredients using the smartphone app. The reception unit also allows a user to input preferences using the smartphone app. The reception unit also allows a user to input allergy information using the smartphone app. This allows a user to input information using the smartphone app. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the information input using the smartphone app to a generation AI and have the generation AI analyze the information.

[0031] The analysis unit can analyze the user's input information using machine learning. Machine learning includes, but is not limited to, algorithms such as neural networks and support vector machines. For example, the analysis unit can analyze the user's input information using neural networks. The analysis unit can also analyze the user's input information using support vector machines. The analysis unit can also analyze the user's input information using data mining technology. In this way, the use of machine learning makes it possible to analyze the user's input information with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input information to a generation AI and have the generation AI analyze the information.

[0032] The generation unit can generate a menu based on nutritional balance, cooking time, and seasonal ingredients. Nutritional balance includes, for example, the recommended intake amount of each nutrient and how to balance it, but is not limited to such examples. For example, the generation unit can generate a menu based on the recommended intake amount of each nutrient. The generation unit can also generate a menu based on a balanced diet. Cooking time includes, for example, the estimated cooking time for each dish and cooking procedures, but is not limited to such examples. For example, the generation unit can generate a menu based on the estimated cooking time for each dish. The generation unit can also generate a menu based on cooking procedures. Seasonal ingredients include, for example, a list of seasonal ingredients and the characteristics of ingredients by season, but are not limited to such examples. For example, the generation unit can generate a menu based on a list of seasonal ingredients. The generation unit can also generate a menu based on the characteristics of ingredients by season. This makes it possible to generate a menu that takes into account nutritional balance, cooking time, seasonal ingredients, etc. Some or all of the above-mentioned processing by the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input user input information into the generation AI and cause the generation AI to generate a menu.

[0033] The providing unit can notify the user's smartphone of the generated menu. Notification methods include, but are not limited to, push notification, email notification, and in-app notification. For example, the providing unit can notify the user's smartphone of the generated menu by push notification. The providing unit can also notify the user's smartphone of the generated menu by email notification. The providing unit can also notify the user's smartphone of the generated menu by in-app notification. In this way, the generated menu can be notified to the user's smartphone. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated menu into a generation AI and have the generation AI generate a notification.

[0034] The providing unit can display the generated menu within the app. Display within the app includes, but is not limited to, for example, a display layout, a user interface design, and the like. For example, the providing unit can provide a display layout for displaying the generated menu within the app. The providing unit can also provide a user interface design for displaying the generated menu within the app. The providing unit can also provide a customizable display method for displaying the generated menu within the app. This allows the generated menu to be displayed within the app. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated menu into a generation AI and cause the generation AI to generate a display.

[0035] The generation unit can generate a menu based on the user's preferences and allergy information. User preferences include, but are not limited to, favorite dishes, disliked dishes, and cooking method preferences. For example, the generation unit can generate a menu based on the user's favorite dishes. The generation unit can also generate a menu that avoids dishes the user dislikes. The generation unit can also generate a menu based on the user's cooking method preferences. Allergy information includes, but is not limited to, nut allergies, dairy allergies, gluten allergies, and the like. For example, the generation unit can generate a nut-free menu for a user with a nut allergy. The generation unit can also generate a dairy-free menu for a user with a dairy allergy. The generation unit can also generate a gluten-free menu for a user with a gluten allergy. This allows a menu to be generated that takes into account the user's preferences and allergy information. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user preferences and allergy information into the generation AI and have the generation AI generate a menu.

[0036] The providing unit can notify the user that the generated menu is based on the user's preferences and nutritional balance. Examples of notification include, but are not limited to, push notification, email notification, and in-app notification. For example, the providing unit can notify the user that the generated menu takes into account the user's preferences and nutritional balance via a push notification. The providing unit can also notify the user that the generated menu takes into account the user's preferences and nutritional balance via an email notification. The providing unit can also notify the user that the generated menu takes into account the user's preferences and nutritional balance via an in-app notification. This allows the user to be notified that the generated menu takes into account the user's preferences and nutritional balance. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the generated menu into a generation AI and cause the generation AI to generate a notification.

[0037] The reception unit can analyze the user's past input history and suggest the optimal input method. The input history includes, for example, past input data, input frequency, etc., but is not limited to such examples. For example, the reception unit can automatically display ingredients and preferences that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and preferences to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0038] When receiving information, the reception unit can filter the information based on the user's current health condition and dietary history. Examples of health conditions include, but are not limited to, medical data and self-reported data. For example, if the user inputs the results of a recent health check, the reception unit can suggest appropriate ingredients based on the information. The reception unit can also analyze the user's past dietary history and suggest ingredients that take nutritional balance into consideration. The reception unit can also suggest appropriate ingredients and dishes based on the user's current physical condition (e.g., cold, fatigue). Examples of dietary history include, but are not limited to, a food recording app or a handwritten food diary. For example, the reception unit can suggest ingredients that take nutritional balance into consideration based on the user's past dietary history. The reception unit can also analyze the user's dietary history and suggest ingredients that contain specific nutrients. The reception unit can also suggest combinations of ingredients based on the user's dietary history. This makes it possible to filter appropriate information based on the user's health condition and dietary history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's health condition and dietary history into the generation AI and have the generation AI perform filtering.

[0039] When receiving information, the reception unit can select an optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs ingredients or preferences by voice, the reception unit can accept the information using voice recognition technology. Furthermore, when a user inputs information by text, the reception unit can accept the information using text analysis technology. Furthermore, when a user inputs ingredients by image, the reception unit can accept the information using image recognition technology. The optimal input means can include, but are not limited to, selection criteria based on the user's situation or environment. For example, when a user prefers voice input, the reception unit can provide voice input with priority. Furthermore, when a user prefers text input, the reception unit can provide text input with priority. Furthermore, when a user prefers image input, the reception unit can provide image input with priority. This allows the optimal input means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0040] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user lives in a specific area, the reception unit can prioritize suggesting ingredients that are easily available in that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the reception unit can prioritize suggesting ingredients and dishes from that area. Examples of highly relevant information include, but are not limited to, information based on the user's current location. For example, the reception unit can suggest highly relevant ingredients and dishes based on the user's current location. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Furthermore, the reception unit can suggest ingredients and dishes from a specific area based on the user's geographical location information. This allows highly relevant information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant information.

[0041] When receiving information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the reception unit can suggest related ingredients and dishes based on information about dishes shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest ingredients and dishes that match the user's preferences. The reception unit can also suggest related ingredients and dishes based on the activities of the user's friends on social media. The related information can include, for example, social media trend information, but is not limited to these examples. For example, the reception unit can suggest related ingredients and dishes based on the user's social media activity. The reception unit can also accept related information based on social media trend information. The reception unit can also suggest related information based on the activities of the user's friends on social media. In this way, related information can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into the generation AI and have the generation AI suggest related information.

[0042] When receiving information, the reception unit can customize the input method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user ratings and comments. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest more appropriate methods. Examples of customizing the input method include, but are not limited to, changing the color or the input method. For example, the reception unit can change the color of the input interface based on the user's past feedback. The reception unit can also change the input method based on the user's past feedback. The reception unit can also improve the input procedure based on the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback to a generation AI and cause the generation AI to customize the input method.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the freshness and storage state of the ingredients. The freshness of ingredients includes, but is not limited to, the expiration date and storage state. For example, the analysis unit can suggest the optimal dish based on the freshness information of the ingredients input by the user. Furthermore, if the user inputs the storage state, the analysis unit can suggest an appropriate dish based on that information. Furthermore, if the user inputs the purchase date of the ingredients, the analysis unit can suggest a dish that takes freshness into consideration based on that information. The storage state includes, but is not limited to, for example, refrigerated storage, frozen storage, etc. For example, the analysis unit can suggest the optimal dish based on the storage state input by the user. Furthermore, the analysis unit can suggest an appropriate dish based on ingredients that the user has stored in the refrigerator. Furthermore, the analysis unit can suggest an appropriate dish based on ingredients that the user has stored in the freezer. This improves the accuracy of the analysis by taking into consideration the freshness and storage state of the ingredients. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the freshness and storage conditions of the user's ingredients into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. Meal history includes, but is not limited to, examples such as a meal recording app and a handwritten food diary. For example, the analysis unit can suggest related dishes based on the user's past favorite dishes. The analysis unit can also analyze the user's past meal history and suggest dishes that take nutritional balance into consideration. The analysis unit can also suggest appropriate dishes based on ingredients the user has avoided in the past. This can improve the accuracy of the analysis based on the user's past meal history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can perform analysis based on the user's health data. Health data includes, but is not limited to, blood pressure, blood sugar levels, and allergy information. For example, the analysis unit can suggest dishes with an appropriate nutritional balance based on the health data input by the user. The analysis unit can also suggest dishes containing specific nutrients, taking into account the user's health condition. The analysis unit can also suggest dishes suitable for maintaining health based on the user's health data. This allows analysis to be performed based on the user's health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's health data into a generation AI and have the generation AI perform the analysis.

[0046] The analysis unit can perform the analysis while taking into account the geographical food culture of the user. Geographical food culture includes, but is not limited to, regional eating habits and traditional cuisine, for example. For example, if the user lives in a specific region, the analysis unit can suggest dishes that take into account the food culture of that region. Furthermore, if the user is traveling, the analysis unit can suggest dishes that take into account the food culture of the travel destination. Furthermore, if the user prefers cuisine from a specific region, the analysis unit can suggest dishes that take into account the food culture of that region. This allows the analysis to take into account the geographical food culture of the user. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical food culture information into the generation AI and have the generation AI perform the analysis.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's ingredient purchase history. The ingredient purchase history includes, but is not limited to, receipt data, online purchase history, etc. For example, the analysis unit can suggest related dishes based on ingredients previously purchased by the user. The analysis unit can also analyze the user's purchase history and suggest dishes that take nutritional balance into consideration. If the user wants to avoid ingredients previously purchased, the analysis unit can suggest appropriate dishes based on that information. This improves the accuracy of the analysis based on the user's ingredient purchase history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's ingredient purchase history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0048] During analysis, the analysis unit can perform analysis based on the user's family composition and meal sharing information. Family composition includes, for example, the number of family members and the age composition, but is not limited to these examples. For example, when a user eats with their family, the analysis unit can suggest dishes that take into account the preferences of all family members. Furthermore, when a user shares a meal with friends, the analysis unit can suggest dishes that everyone can enjoy. Furthermore, the analysis unit can suggest appropriate portions of dishes based on the user's family composition. Meal sharing information includes, for example, the number of family meals and the frequency of sharing, but is not limited to these examples. For example, the analysis unit can suggest appropriate dishes based on the user's family composition and meal sharing information. Furthermore, the analysis unit can suggest dishes that take nutritional balance into consideration based on the user's family composition. Furthermore, the analysis unit can suggest appropriate dishes based on the user's meal sharing information. In this way, analysis can be performed based on the user's family composition and meal sharing information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's family structure and meal sharing information into the generation AI and have the generation AI perform the analysis.

[0049] When generating a menu, the generation unit can adjust the menu based on the nutritional value and calories of ingredients. Nutritional value includes, for example, the content of each nutrient and the recommended intake amount, but is not limited to such examples. For example, the generation unit can propose a balanced menu based on the nutritional value of ingredients input by the user. Furthermore, if the user is on a calorie restriction, the generation unit can also propose a low-calorie menu. Furthermore, if the user wants to consume a specific nutrient, the generation unit can also propose a menu containing that nutrient. Calories include, for example, but are not limited to such examples. For example, the generation unit can propose an appropriate menu based on the calorie calculation method of each dish. Furthermore, if the user is on a calorie restriction, the generation unit can also propose a low-calorie menu. Furthermore, if the user wants to consume a specific number of calories, the generation unit can also propose a menu containing that calorie. This allows the menu to be adjusted taking into account the nutritional value and calories of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the nutritional value and calorie information of the user's ingredients into the generation AI and have the generation AI adjust the menu.

[0050] When generating a menu, the generation unit can improve the accuracy of the menu by referring to the user's past meal history. Examples of meal history include, but are not limited to, a meal recording app or a handwritten food diary. For example, the generation unit can suggest related menus based on dishes that the user has previously liked. The generation unit can also analyze the user's past meal history and suggest menus that take nutritional balance into consideration. The generation unit can also suggest appropriate menus based on ingredients that the user has avoided in the past. This can improve the accuracy of the menu based on the user's past meal history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the menu.

[0051] When generating a menu, the generation unit can generate the menu based on the user's health data. Health data includes, but is not limited to, allergy information, medical history, and medical data, for example. For example, if the user inputs allergy information, the generation unit can suggest a menu that avoids allergies based on that information. The generation unit can also suggest a menu that includes specific nutrients, taking into account the user's medical history. The generation unit can also suggest a menu that is suitable for maintaining health based on the user's health data. In this way, a menu can be generated based on the user's health data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI generate a menu.

[0052] When generating a menu, the generation unit can suggest special menus that match seasonal events or holidays. Seasonal events and holidays include, but are not limited to, Christmas, New Year's, Halloween, etc. For example, the generation unit can suggest special menus that match seasonal events (e.g., Christmas, Halloween). The generation unit can also suggest special menus that match holidays or anniversaries. The generation unit can also suggest special menus that use seasonal ingredients. This makes it possible to suggest special menus that match seasonal events or holidays. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about seasonal events or holidays into the generation AI and cause the generation AI to suggest special menus.

[0053] When generating a menu, the generation unit can generate the menu by referring to the user's ingredient purchase history. The ingredient purchase history includes, but is not limited to, receipt data, online purchase history, etc. For example, the generation unit can suggest related menus based on ingredients previously purchased by the user. The generation unit can also analyze the user's purchase history and suggest menus that take nutritional balance into consideration. If the user wishes to avoid ingredients previously purchased, the generation unit can suggest appropriate menus based on that information. This allows the generation of menus based on the user's ingredient purchase history. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's ingredient purchase history into the generation AI and have the generation AI generate the menu.

[0054] When generating a menu, the generation unit can generate the menu based on the user's family structure and meal sharing information. Family structure includes, for example, the number of family members and the age composition, but is not limited to these examples. For example, when a user eats with their family, the generation unit can propose a menu that takes into account the preferences of all family members. Furthermore, when a user shares a meal with friends, the generation unit can propose a menu that everyone can enjoy. Furthermore, the generation unit can propose a menu with appropriate portions based on the user's family structure. Meal sharing information includes, for example, the number of family meals and the frequency of sharing, but is not limited to these examples. For example, the generation unit can propose an appropriate menu based on the user's family structure and meal sharing information. Furthermore, the generation unit can propose a menu that takes nutritional balance into consideration based on the user's family structure. Furthermore, the generation unit can propose an appropriate menu based on the user's meal sharing information. In this way, a menu can be generated based on the user's family structure and meal sharing information. Some or all of the above-described processing by the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input the user's family structure and meal sharing information into the generation AI and have the generation AI generate a menu.

[0055] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the providing unit can prioritize delivery methods (e.g., notification, email) that the user has previously preferred. The providing unit can also improve the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously dissatisfied with and suggest more appropriate methods. Customizing the delivery method includes, but is not limited to, changing the notification method and the display method. For example, the providing unit can change the color of the delivery interface based on the user's past feedback. The providing unit can also change the notification method based on the user's past feedback. The providing unit can also change the display method based on the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the delivery method.

[0056] The providing unit can select the optimal delivery means by taking into account the user's device information when providing the notification. Device information includes, but is not limited to, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the providing unit can prioritize in-app notifications. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. The optimal delivery means can include, but is not limited to, a delivery method based on the type of device. For example, the providing unit can select the optimal delivery means based on the user's device information. Furthermore, the providing unit can select a notification method based on the user's device information. Furthermore, the providing unit can select a display method based on the user's device information. This allows the optimal delivery means to be selected based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery means.

[0057] The providing unit can adjust the timing of provision based on the user's current schedule and lifestyle. Examples of schedules include, but are not limited to, a calendar app or a handwritten schedule book. For example, the providing unit can refer to the user's schedule and provide a menu at an appropriate time. Furthermore, the providing unit can provide menus at breakfast, lunch, and dinner times, taking into account the user's lifestyle. Furthermore, if the user is busy during a specific time period, the providing unit can provide a menu that avoids that time period. Examples of lifestyles include, but are not limited to, sleep patterns and activity times. For example, the providing unit can provide a menu at an appropriate time based on the user's lifestyle. Furthermore, the providing unit can adjust the notification time period based on the user's lifestyle. Furthermore, the providing unit can adjust the frequency of notifications based on the user's lifestyle. Examples of provision timing include, but are not limited to, notification time periods and frequency. For example, the providing unit can provide a menu at an appropriate time based on the user's schedule and lifestyle. Furthermore, the providing unit can adjust the notification time period based on the user's schedule and lifestyle. The providing unit can also adjust the frequency of notifications based on the user's schedule and lifestyle. This allows the timing of notification to be adjusted according to the user's schedule and lifestyle. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's schedule and lifestyle into the generating AI and cause the generating AI to adjust the timing of notification.

[0058] The providing unit can provide a menu with high relevance based on the user's geographical location information at the time of providing the menu. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user lives in a specific area, the providing unit can provide a menu with high relevance based on ingredients that are easily available in that area. Furthermore, if the user is traveling, the providing unit can provide a menu with high relevance based on ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the providing unit can provide ingredients and cuisine from that area with high relevance. Examples of highly relevance based on the user's geographical location information include, but are not limited to, regional food culture and seasonal ingredients. For example, the providing unit can provide a menu with high relevance based on the user's geographical location information. Furthermore, the providing unit can provide a menu that takes regional food culture into consideration based on the user's geographical location information. Furthermore, the providing unit can provide a menu with high relevance based on the user's geographical location information. In this way, a menu with high relevance based on the user's geographical location information can be provided with high relevance based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generating AI and cause the generating AI to provide highly relevant menus.

[0059] The providing unit can analyze the user's social media activity and provide related menus when providing the menu. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the providing unit can suggest related menus based on information about dishes shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and suggest menus that match the user's preferences. The providing unit can also suggest related menus based on the activities of the user's friends on social media. Related menus include, for example, social media trend information, but are not limited to these examples. For example, the providing unit can suggest related menus based on the user's social media activity. The providing unit can also provide related menus based on social media trend information. The providing unit can also provide related menus based on the activities of the user's friends on social media. In this way, related menus can be provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the user's social media activity into the generation AI and have the generation AI provide related menus.

[0060] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the providing unit can prioritize delivery methods (e.g., notification, email) that the user has previously preferred. The providing unit can also improve the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously dissatisfied with and suggest more appropriate methods. Customizing the delivery method includes, but is not limited to, changing the notification method and the display method. For example, the providing unit can change the color of the delivery interface based on the user's past feedback. The providing unit can also change the notification method based on the user's past feedback. The providing unit can also change the display method based on the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the delivery method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can perform analysis taking into account the user's storage method for ingredients. For example, it can suggest appropriate cooking methods and storage periods based on information such as refrigerated, frozen, or room temperature storage. Furthermore, when the user inputs the storage method, the analysis unit can suggest optimal dishes based on that information. For example, it can suggest dishes using frozen chicken or dishes using room temperature vegetables. This can improve the accuracy of analysis by taking into account the storage method for ingredients.

[0063] The generation unit can generate menus based on the user's ingredient purchase history, taking into account the expiration dates of ingredients. For example, it can propose menus that prioritize ingredients that should be consumed early based on the number of days that have passed since the purchase date. It can also propose dishes that use ingredients that are close to their expiration date. This reduces food waste and allows for efficient consumption.

[0064] The provision unit can propose menus using local specialties based on the user's geographical location information. For example, if the user lives in a specific area, it can propose dishes using local specialties that are easily available in that area. Also, if the user is traveling, it can propose dishes using local specialties from the travel destination. This makes it possible to provide menus that utilize local specialties.

[0065] The analysis unit can suggest ingredient combinations based on the user's past meal history. For example, it can suggest new dishes based on ingredient combinations that the user liked in the past. It can also suggest appropriate dishes based on ingredient combinations that the user avoided in the past. In this way, it is possible to suggest ingredient combinations based on the user's past meal history.

[0066] The generation unit can suggest menus that are fortified with specific nutrients based on the user's health data. For example, if the user is iron deficient, it can suggest dishes that use ingredients that are high in iron. Also, if the user wants to take in a lot of vitamin C, it can suggest dishes that are high in that nutrient. In this way, it is possible to suggest menus that are fortified with specific nutrients based on the user's health data.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit accepts information entered by the user. The information entered by the user includes, for example, ingredients, preferences, and allergy information. The reception unit allows the user to enter information using a smartphone app, and can also support multiple input methods, such as voice input, text input, and image input. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is carried out using machine learning algorithms and data mining technology, taking into account nutritional balance, cooking time, seasonal ingredients, etc. based on the information entered by the user. Step 3: The generation unit generates a menu based on the information analyzed by the analysis unit. The menu is generated taking into consideration nutritional balance, cooking time, seasonal ingredients, etc., and can also generate a menu taking into consideration the user's preferences and allergy information. Step 4: The provision unit provides the menu generated by the generation unit. This can be done by notifying the user on their smartphone or by displaying it within the app, and can also notify the user that the generated menu takes into consideration the user's preferences and nutritional balance.

[0069] (Example 2) A menu generation system according to an embodiment of the present invention generates and provides an optimal menu based on information input by a user. In the menu generation system, the user inputs information such as ingredients, preferences, and allergy information, and AI analyzes this information to generate an optimal menu. The generated menu takes into account the user's preferences and nutritional balance, enriching daily meals. For example, a user inputs information such as ingredients, preferences, and allergy information. The user inputs information such as the ingredients they own, their favorite dishes, and whether or not they have any allergies. For example, the user inputs information such as "I have chicken, tomatoes, and broccoli," "I like Japanese food," and "I have a nut allergy." This information is input into AI. The AI ​​then analyzes the input information and generates an optimal menu for the user. Based on the information input by the user, the AI ​​generates a menu taking into account nutritional balance, cooking time, seasonal ingredients, and other factors. For example, a menu such as "chicken and tomato stew" or "broccoli salad" may be generated. The generated menu takes into account the user's preferences and nutritional balance. For example, a user who likes Japanese food may be suggested a menu centered on Japanese food, while a user with a nut allergy may be suggested a menu that does not include nuts. In addition, the main dish, side dish, soup, etc. are combined in a balanced manner, taking nutritional balance into consideration. This system allows users to enrich their daily meals. For example, on busy days, easy-to-make menus are suggested, and on days when you have time, elaborate dishes are suggested. In addition, menus using seasonal ingredients are suggested, allowing you to enjoy the feeling of the seasons. Furthermore, menus that take nutritional balance into consideration are suggested, allowing you to eat a healthy diet. In this way, the menu generation system can generate and provide optimal menus based on user information. For example, by generating menus that take nutritional balance, cooking time, seasonal ingredients, etc. into consideration based on information entered by the user, it is possible to enrich the user's meals and support a healthy diet.

[0070] A menu generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information input by a user. The information input by the user includes, but is not limited to, ingredients, preferences, and allergy information. The reception unit allows the user to input information using, for example, a smartphone app. The reception unit can also support multiple input methods, such as voice input, text input, and image input. The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using, for example, a machine learning algorithm or data mining technology, but is not limited to, examples. For example, the analysis unit performs analysis based on the information input by the user, taking into account nutritional balance, cooking time, seasonal ingredients, and the like. The generation unit generates a menu based on the information analyzed by the analysis unit. The generation is performed, for example, taking into account nutritional balance, cooking time, seasonal ingredients, and the like, but is not limited to, examples. For example, the generation unit can generate a menu taking into account the user's preferences and allergy information. The provision unit provides the menu generated by the generation unit. The provision of the information may be, for example, by notifying the user on their smartphone or by displaying the information within an app, but is not limited to these examples. For example, the provision unit may notify the user that the generated menu takes into consideration the user's preferences and nutritional balance. This allows the menu generation system according to the embodiment to generate and provide an optimal menu based on user information. For example, by generating a menu that takes into consideration nutritional balance, cooking time, seasonal ingredients, etc. based on information input by the user, the system can enrich the user's meals and support a healthy eating lifestyle.

[0071] The reception unit allows a user to input information using a smartphone app. The smartphone app includes functions such as, but not limited to, inputting ingredients, preferences, and allergy information. For example, the reception unit allows a user to input ingredients using the smartphone app. The reception unit also allows a user to input preferences using the smartphone app. The reception unit also allows a user to input allergy information using the smartphone app. This allows a user to input information using the smartphone app. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the information input using the smartphone app to a generation AI and have the generation AI analyze the information.

[0072] The analysis unit can analyze the user's input information using machine learning. Machine learning includes, but is not limited to, algorithms such as neural networks and support vector machines. For example, the analysis unit can analyze the user's input information using neural networks. The analysis unit can also analyze the user's input information using support vector machines. The analysis unit can also analyze the user's input information using data mining technology. In this way, the use of machine learning makes it possible to analyze the user's input information with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input information to a generation AI and have the generation AI analyze the information.

[0073] The generation unit can generate a menu based on nutritional balance, cooking time, and seasonal ingredients. Nutritional balance includes, for example, the recommended intake amount of each nutrient and how to balance it, but is not limited to such examples. For example, the generation unit can generate a menu based on the recommended intake amount of each nutrient. The generation unit can also generate a menu based on a balanced diet. Cooking time includes, for example, the estimated cooking time for each dish and cooking procedures, but is not limited to such examples. For example, the generation unit can generate a menu based on the estimated cooking time for each dish. The generation unit can also generate a menu based on cooking procedures. Seasonal ingredients include, for example, a list of seasonal ingredients and the characteristics of ingredients by season, but are not limited to such examples. For example, the generation unit can generate a menu based on a list of seasonal ingredients. The generation unit can also generate a menu based on the characteristics of ingredients by season. This makes it possible to generate a menu that takes into account nutritional balance, cooking time, seasonal ingredients, etc. Some or all of the above-mentioned processing by the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input user input information into the generation AI and cause the generation AI to generate a menu.

[0074] The providing unit can notify the user's smartphone of the generated menu. Notification methods include, but are not limited to, push notification, email notification, and in-app notification. For example, the providing unit can notify the user's smartphone of the generated menu by push notification. The providing unit can also notify the user's smartphone of the generated menu by email notification. The providing unit can also notify the user's smartphone of the generated menu by in-app notification. In this way, the generated menu can be notified to the user's smartphone. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated menu into a generation AI and have the generation AI generate a notification.

[0075] The providing unit can display the generated menu within the app. Display within the app includes, but is not limited to, for example, a display layout, a user interface design, and the like. For example, the providing unit can provide a display layout for displaying the generated menu within the app. The providing unit can also provide a user interface design for displaying the generated menu within the app. The providing unit can also provide a customizable display method for displaying the generated menu within the app. This allows the generated menu to be displayed within the app. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated menu into a generation AI and cause the generation AI to generate a display.

[0076] The generation unit can generate a menu based on the user's preferences and allergy information. User preferences include, but are not limited to, favorite dishes, disliked dishes, and cooking method preferences. For example, the generation unit can generate a menu based on the user's favorite dishes. The generation unit can also generate a menu that avoids dishes the user dislikes. The generation unit can also generate a menu based on the user's cooking method preferences. Allergy information includes, but is not limited to, nut allergies, dairy allergies, gluten allergies, and the like. For example, the generation unit can generate a nut-free menu for a user with a nut allergy. The generation unit can also generate a dairy-free menu for a user with a dairy allergy. The generation unit can also generate a gluten-free menu for a user with a gluten allergy. This allows a menu to be generated that takes into account the user's preferences and allergy information. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user preferences and allergy information into the generation AI and have the generation AI generate a menu.

[0077] The providing unit can notify the user that the generated menu is based on the user's preferences and nutritional balance. Examples of notification include, but are not limited to, push notification, email notification, and in-app notification. For example, the providing unit can notify the user that the generated menu takes into account the user's preferences and nutritional balance via a push notification. The providing unit can also notify the user that the generated menu takes into account the user's preferences and nutritional balance via an email notification. The providing unit can also notify the user that the generated menu takes into account the user's preferences and nutritional balance via an in-app notification. This allows the user to be notified that the generated menu takes into account the user's preferences and nutritional balance. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the generated menu into a generation AI and cause the generation AI to generate a notification.

[0078] The reception unit can estimate the user's emotions and customize the information input interface based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can estimate the user's emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. The reception unit can also estimate the user's emotions using text analysis technology. Input interface customization can include, but is not limited to, changing the color or the input method. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This allows the information input interface to be customized according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to customize the input interface.

[0079] The reception unit can analyze the user's past input history and suggest the optimal input method. The input history includes, for example, past input data, input frequency, etc., but is not limited to such examples. For example, the reception unit can automatically display ingredients and preferences that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and preferences to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0080] When receiving information, the reception unit can filter the information based on the user's current health condition and dietary history. Examples of health conditions include, but are not limited to, medical data and self-reported data. For example, if the user inputs the results of a recent health check, the reception unit can suggest appropriate ingredients based on the information. The reception unit can also analyze the user's past dietary history and suggest ingredients that take nutritional balance into consideration. The reception unit can also suggest appropriate ingredients and dishes based on the user's current physical condition (e.g., cold, fatigue). Examples of dietary history include, but are not limited to, a food recording app or a handwritten food diary. For example, the reception unit can suggest ingredients that take nutritional balance into consideration based on the user's past dietary history. The reception unit can also analyze the user's dietary history and suggest ingredients that contain specific nutrients. The reception unit can also suggest combinations of ingredients based on the user's dietary history. This makes it possible to filter appropriate information based on the user's health condition and dietary history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's health condition and dietary history into the generation AI and have the generation AI perform filtering.

[0081] When receiving information, the reception unit can select an optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs ingredients or preferences by voice, the reception unit can accept the information using voice recognition technology. Furthermore, when a user inputs information by text, the reception unit can accept the information using text analysis technology. Furthermore, when a user inputs ingredients by image, the reception unit can accept the information using image recognition technology. The optimal input means can include, but are not limited to, selection criteria based on the user's situation or environment. For example, when a user prefers voice input, the reception unit can provide voice input with priority. Furthermore, when a user prefers text input, the reception unit can provide text input with priority. Furthermore, when a user prefers image input, the reception unit can provide image input with priority. This allows the optimal input means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0082] The reception unit can estimate the user's emotion and determine the priority of input information based on the estimated user's emotion. Emotion estimation can be achieved using, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can estimate the user's emotion using facial expression recognition. The reception unit can also estimate the user's emotion using voice analysis. The reception unit can also estimate the user's emotion using text analysis. The priority of input information can be determined based on, but is not limited to, importance and urgency. For example, if the user is stressed, the reception unit can prioritize input of important information. If the user is relaxed, the reception unit can prioritize input of detailed information. If the user is in a hurry, the reception unit can prioritize input of the minimum necessary information. This allows the priority of input information to be determined according to the user's emotion. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI determine the priority of the input information.

[0083] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user lives in a specific area, the reception unit can prioritize suggesting ingredients that are easily available in that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the reception unit can prioritize suggesting ingredients and dishes from that area. Examples of highly relevant information include, but are not limited to, information based on the user's current location. For example, the reception unit can suggest highly relevant ingredients and dishes based on the user's current location. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Furthermore, the reception unit can suggest ingredients and dishes from a specific area based on the user's geographical location information. This allows highly relevant information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant information.

[0084] When receiving information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the reception unit can suggest related ingredients and dishes based on information about dishes shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest ingredients and dishes that match the user's preferences. The reception unit can also suggest related ingredients and dishes based on the activities of the user's friends on social media. The related information can include, for example, social media trend information, but is not limited to these examples. For example, the reception unit can suggest related ingredients and dishes based on the user's social media activity. The reception unit can also accept related information based on social media trend information. The reception unit can also suggest related information based on the activities of the user's friends on social media. In this way, related information can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into the generation AI and have the generation AI suggest related information.

[0085] When receiving information, the reception unit can customize the input method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user ratings and comments. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest more appropriate methods. Examples of customizing the input method include, but are not limited to, changing the color or the input method. For example, the reception unit can change the color of the input interface based on the user's past feedback. The reception unit can also change the input method based on the user's past feedback. The reception unit can also improve the input procedure based on the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback to a generation AI and cause the generation AI to customize the input method.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can also estimate the user's emotions using text analysis technology. Adjusting the analysis algorithm can include, but is not limited to, parameter changes and model retraining. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to provide more options. If the user is in a hurry, the analysis unit can perform a quick analysis to provide optimal options. If the user is stressed, the analysis unit can perform a simple analysis to provide intuitive options. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis based on the freshness and storage state of the ingredients. The freshness of ingredients includes, but is not limited to, the expiration date and storage state. For example, the analysis unit can suggest the optimal dish based on the freshness information of the ingredients input by the user. Furthermore, if the user inputs the storage state, the analysis unit can suggest an appropriate dish based on that information. Furthermore, if the user inputs the purchase date of the ingredients, the analysis unit can suggest a dish that takes freshness into consideration based on that information. The storage state includes, but is not limited to, for example, refrigerated storage, frozen storage, etc. For example, the analysis unit can suggest the optimal dish based on the storage state input by the user. Furthermore, the analysis unit can suggest an appropriate dish based on ingredients that the user has stored in the refrigerator. Furthermore, the analysis unit can suggest an appropriate dish based on ingredients that the user has stored in the freezer. This improves the accuracy of the analysis by taking into consideration the freshness and storage state of the ingredients. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the freshness and storage conditions of the user's ingredients into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. Meal history includes, but is not limited to, examples such as a meal recording app and a handwritten food diary. For example, the analysis unit can suggest related dishes based on the user's past favorite dishes. The analysis unit can also analyze the user's past meal history and suggest dishes that take nutritional balance into consideration. The analysis unit can also suggest appropriate dishes based on ingredients the user has avoided in the past. This can improve the accuracy of the analysis based on the user's past meal history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] During analysis, the analysis unit can perform analysis based on the user's health data. Health data includes, but is not limited to, blood pressure, blood sugar levels, and allergy information. For example, the analysis unit can suggest dishes with an appropriate nutritional balance based on the health data input by the user. The analysis unit can also suggest dishes containing specific nutrients, taking into account the user's health condition. The analysis unit can also suggest dishes suitable for maintaining health based on the user's health data. This allows analysis to be performed based on the user's health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's health data into a generation AI and have the generation AI perform the analysis.

[0090] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. Emotion estimation can be achieved using, but is not limited to, facial expression recognition, voice analysis, text analysis, and other techniques. For example, the analysis unit can estimate the user's emotion using facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion using text analysis. Methods for displaying the analysis results can include, but are not limited to, graph display, text display, and the like. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotion. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0091] The analysis unit can perform the analysis while taking into account the geographical food culture of the user. Geographical food culture includes, but is not limited to, regional eating habits and traditional cuisine, for example. For example, if the user lives in a specific region, the analysis unit can suggest dishes that take into account the food culture of that region. Furthermore, if the user is traveling, the analysis unit can suggest dishes that take into account the food culture of the travel destination. Furthermore, if the user prefers cuisine from a specific region, the analysis unit can suggest dishes that take into account the food culture of that region. This allows the analysis to take into account the geographical food culture of the user. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical food culture information into the generation AI and have the generation AI perform the analysis.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's ingredient purchase history. The ingredient purchase history includes, but is not limited to, receipt data, online purchase history, etc. For example, the analysis unit can suggest related dishes based on ingredients previously purchased by the user. The analysis unit can also analyze the user's purchase history and suggest dishes that take nutritional balance into consideration. If the user wants to avoid ingredients previously purchased, the analysis unit can suggest appropriate dishes based on that information. This improves the accuracy of the analysis based on the user's ingredient purchase history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's ingredient purchase history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0093] During analysis, the analysis unit can perform analysis based on the user's family composition and meal sharing information. Family composition includes, for example, the number of family members and the age composition, but is not limited to these examples. For example, when a user eats with their family, the analysis unit can suggest dishes that take into account the preferences of all family members. Furthermore, when a user shares a meal with friends, the analysis unit can suggest dishes that everyone can enjoy. Furthermore, the analysis unit can suggest appropriate portions of dishes based on the user's family composition. Meal sharing information includes, for example, the number of family meals and the frequency of sharing, but is not limited to these examples. For example, the analysis unit can suggest appropriate dishes based on the user's family composition and meal sharing information. Furthermore, the analysis unit can suggest dishes that take nutritional balance into consideration based on the user's family composition. Furthermore, the analysis unit can suggest appropriate dishes based on the user's meal sharing information. In this way, analysis can be performed based on the user's family composition and meal sharing information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's family structure and meal sharing information into the generation AI and have the generation AI perform the analysis.

[0094] The generation unit can estimate the user's emotions and adjust the presentation method of the menu based on the estimated user's emotions. Emotion estimation can be achieved using, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit can also estimate the user's emotions using text analysis technology. Menu presentation methods can include, but are not limited to, text display, image display, and video display. For example, the generation unit can provide a menu with detailed explanations when the user is relaxed. The generation unit can provide a concise and to-the-point menu when the user is in a hurry. The generation unit can also provide a visually appealing menu when the user is stressed. This allows the presentation method of the menu to be adjusted according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the way the menu is presented.

[0095] When generating a menu, the generation unit can adjust the menu based on the nutritional value and calories of ingredients. Nutritional value includes, for example, the content of each nutrient and the recommended intake amount, but is not limited to such examples. For example, the generation unit can propose a balanced menu based on the nutritional value of ingredients input by the user. Furthermore, if the user is on a calorie restriction, the generation unit can also propose a low-calorie menu. Furthermore, if the user wants to consume a specific nutrient, the generation unit can also propose a menu containing that nutrient. Calories include, for example, but are not limited to such examples. For example, the generation unit can propose an appropriate menu based on the calorie calculation method of each dish. Furthermore, if the user is on a calorie restriction, the generation unit can also propose a low-calorie menu. Furthermore, if the user wants to consume a specific number of calories, the generation unit can also propose a menu containing that calorie. This allows the menu to be adjusted taking into account the nutritional value and calories of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the nutritional value and calorie information of the user's ingredients into the generation AI and have the generation AI adjust the menu.

[0096] When generating a menu, the generation unit can improve the accuracy of the menu by referring to the user's past meal history. Examples of meal history include, but are not limited to, a meal recording app or a handwritten food diary. For example, the generation unit can suggest related menus based on dishes that the user has previously liked. The generation unit can also analyze the user's past meal history and suggest menus that take nutritional balance into consideration. The generation unit can also suggest appropriate menus based on ingredients that the user has avoided in the past. This can improve the accuracy of the menu based on the user's past meal history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the menu.

[0097] When generating a menu, the generation unit can generate the menu based on the user's health data. Health data includes, but is not limited to, allergy information, medical history, and medical data, for example. For example, if the user inputs allergy information, the generation unit can suggest a menu that avoids allergies based on that information. The generation unit can also suggest a menu that includes specific nutrients, taking into account the user's medical history. The generation unit can also suggest a menu that is suitable for maintaining health based on the user's health data. In this way, a menu can be generated based on the user's health data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI generate a menu.

[0098] The generation unit can estimate the user's emotions and adjust the length and level of detail of the menu based on the estimated user's emotions. Emotion estimation can be achieved using, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit can also estimate the user's emotions using text analysis technology. The length and level of detail of the menu can include, but are not limited to, detailed recipes and simple recipes. For example, the generation unit can provide a short, concise menu if the user is in a hurry. The generation unit can provide a longer menu with detailed explanations if the user is relaxed. The generation unit can also provide a visually appealing menu if the user is stressed. This allows the length and level of detail of the menu to be adjusted according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length and level of detail of the menu.

[0099] When generating a menu, the generation unit can suggest special menus that match seasonal events or holidays. Seasonal events and holidays include, but are not limited to, Christmas, New Year's, Halloween, etc. For example, the generation unit can suggest special menus that match seasonal events (e.g., Christmas, Halloween). The generation unit can also suggest special menus that match holidays or anniversaries. The generation unit can also suggest special menus that use seasonal ingredients. This makes it possible to suggest special menus that match seasonal events or holidays. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about seasonal events or holidays into the generation AI and cause the generation AI to suggest special menus.

[0100] When generating a menu, the generation unit can generate the menu by referring to the user's ingredient purchase history. The ingredient purchase history includes, but is not limited to, receipt data, online purchase history, etc. For example, the generation unit can suggest related menus based on ingredients previously purchased by the user. The generation unit can also analyze the user's purchase history and suggest menus that take nutritional balance into consideration. If the user wishes to avoid ingredients previously purchased, the generation unit can suggest appropriate menus based on that information. This allows the generation of menus based on the user's ingredient purchase history. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's ingredient purchase history into the generation AI and have the generation AI generate the menu.

[0101] When generating a menu, the generation unit can generate the menu based on the user's family structure and meal sharing information. Family structure includes, for example, the number of family members and the age composition, but is not limited to these examples. For example, when a user eats with their family, the generation unit can propose a menu that takes into account the preferences of all family members. Furthermore, when a user shares a meal with friends, the generation unit can propose a menu that everyone can enjoy. Furthermore, the generation unit can propose a menu with appropriate portions based on the user's family structure. Meal sharing information includes, for example, the number of family meals and the frequency of sharing, but is not limited to these examples. For example, the generation unit can propose an appropriate menu based on the user's family structure and meal sharing information. Furthermore, the generation unit can propose a menu that takes nutritional balance into consideration based on the user's family structure. Furthermore, the generation unit can propose an appropriate menu based on the user's meal sharing information. In this way, a menu can be generated based on the user's family structure and meal sharing information. Some or all of the above-described processing by the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input the user's family structure and meal sharing information into the generation AI and have the generation AI generate a menu.

[0102] The providing unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user's emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. The providing unit can also estimate the user's emotions using text analysis technology. Menu presentation methods can include, but are not limited to, notification methods and display methods. For example, the providing unit can provide a menu with detailed explanations when the user is relaxed. The providing unit can provide a concise and to-the-point menu when the user is in a hurry. The providing unit can also provide a visually appealing menu when the user is stressed. This allows the menu presentation method to be adjusted according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generating AI and have the generating AI adjust the menu presentation method.

[0103] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the providing unit can prioritize delivery methods (e.g., notification, email) that the user has previously preferred. The providing unit can also improve the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously dissatisfied with and suggest more appropriate methods. Customizing the delivery method includes, but is not limited to, changing the notification method and the display method. For example, the providing unit can change the color of the delivery interface based on the user's past feedback. The providing unit can also change the notification method based on the user's past feedback. The providing unit can also change the display method based on the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the delivery method.

[0104] The providing unit can select the optimal delivery means by taking into account the user's device information when providing the notification. Device information includes, but is not limited to, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the providing unit can prioritize in-app notifications. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. The optimal delivery means can include, but is not limited to, a delivery method based on the type of device. For example, the providing unit can select the optimal delivery means based on the user's device information. Furthermore, the providing unit can select a notification method based on the user's device information. Furthermore, the providing unit can select a display method based on the user's device information. This allows the optimal delivery means to be selected based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery means.

[0105] The providing unit can adjust the timing of provision based on the user's current schedule and lifestyle. Examples of schedules include, but are not limited to, a calendar app or a handwritten schedule book. For example, the providing unit can refer to the user's schedule and provide a menu at an appropriate time. Furthermore, the providing unit can provide menus at breakfast, lunch, and dinner times, taking into account the user's lifestyle. Furthermore, if the user is busy during a specific time period, the providing unit can provide a menu that avoids that time period. Examples of lifestyles include, but are not limited to, sleep patterns and activity times. For example, the providing unit can provide a menu at an appropriate time based on the user's lifestyle. Furthermore, the providing unit can adjust the notification time period based on the user's lifestyle. Furthermore, the providing unit can adjust the frequency of notifications based on the user's lifestyle. Examples of provision timing include, but are not limited to, notification time periods and frequency. For example, the providing unit can provide a menu at an appropriate time based on the user's schedule and lifestyle. Furthermore, the providing unit can adjust the notification time period based on the user's schedule and lifestyle. The providing unit can also adjust the frequency of notifications based on the user's schedule and lifestyle. This allows the timing of notification to be adjusted according to the user's schedule and lifestyle. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's schedule and lifestyle into the generating AI and cause the generating AI to adjust the timing of notification.

[0106] The providing unit can estimate the user's emotions and adjust the order in which the menu items are presented based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. The providing unit can also estimate the user's emotions using text analysis technology. The order in which the menu items are presented can include, but is not limited to, importance and urgency. For example, if the user is relaxed, the providing unit can first present a menu with detailed explanations. If the user is in a hurry, the providing unit can first present a concise and to-the-point menu. If the user is stressed, the providing unit can first present a visually appealing menu. This allows the order in which the menu items are presented to be adjusted according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the order in which the menu is presented.

[0107] The providing unit can provide a menu with high relevance based on the user's geographical location information at the time of providing the menu. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user lives in a specific area, the providing unit can provide a menu with high relevance based on ingredients that are easily available in that area. Furthermore, if the user is traveling, the providing unit can provide a menu with high relevance based on ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the providing unit can provide ingredients and cuisine from that area with high relevance. Examples of highly relevance based on the user's geographical location information include, but are not limited to, regional food culture and seasonal ingredients. For example, the providing unit can provide a menu with high relevance based on the user's geographical location information. Furthermore, the providing unit can provide a menu that takes regional food culture into consideration based on the user's geographical location information. Furthermore, the providing unit can provide a menu with high relevance based on the user's geographical location information. In this way, a menu with high relevance based on the user's geographical location information can be provided with high relevance based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generating AI and cause the generating AI to provide highly relevant menus.

[0108] The providing unit can analyze the user's social media activity and provide related menus when providing the menu. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the providing unit can suggest related menus based on information about dishes shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and suggest menus that match the user's preferences. The providing unit can also suggest related menus based on the activities of the user's friends on social media. Related menus include, for example, social media trend information, but are not limited to these examples. For example, the providing unit can suggest related menus based on the user's social media activity. The providing unit can also provide related menus based on social media trend information. The providing unit can also provide related menus based on the activities of the user's friends on social media. In this way, related menus can be provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the user's social media activity into the generation AI and have the generation AI provide related menus.

[0109] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the providing unit can prioritize delivery methods (e.g., notification, email) that the user has previously preferred. The providing unit can also improve the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously dissatisfied with and suggest more appropriate methods. Customizing the delivery method includes, but is not limited to, changing the notification method and the display method. For example, the providing unit can change the color of the delivery interface based on the user's past feedback. The providing unit can also change the notification method based on the user's past feedback. The providing unit can also change the display method based on the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and a user can input information using a smartphone app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14, and notifies the user's smartphone of the generated menu. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and notifies the user of the generated menu by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and visually notifies the user of the generated menu. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user can input voice information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414, and notifies the user of the generated menu by voice.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can perform analysis taking into account the user's storage method for ingredients. For example, it can suggest appropriate cooking methods and storage periods based on information such as refrigerated, frozen, or room temperature storage. Furthermore, when the user inputs the storage method, the analysis unit can suggest optimal dishes based on that information. For example, it can suggest dishes using frozen chicken or dishes using room temperature vegetables. This can improve the accuracy of analysis by taking into account the storage method for ingredients.

[0112] The provision unit can estimate the user's emotions and adjust the timing of providing a menu based on the estimated user's emotions. For example, if the user is feeling stressed, a menu can be provided at a time when the user is able to relax. Also, if the user is in a hurry, a menu that can be prepared quickly can be provided. Furthermore, if the user is relaxed, a menu that includes detailed explanations can be provided. This makes it possible to adjust the timing of providing a menu according to the user's emotions.

[0113] The generation unit can generate menus based on the user's ingredient purchase history, taking into account the expiration dates of ingredients. For example, it can propose menus that prioritize ingredients that should be consumed early based on the number of days that have passed since the purchase date. It can also propose dishes that use ingredients that are close to their expiration date. This reduces food waste and allows for efficient consumption.

[0114] The analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide more options. If the user is in a hurry, a quick analysis can be performed to provide optimal options. Furthermore, if the user is stressed, a simple analysis can be performed to provide intuitive options. This makes it possible to improve the accuracy of the analysis according to the user's emotions.

[0115] The provision unit can propose menus using local specialties based on the user's geographical location information. For example, if the user lives in a specific area, it can propose dishes using local specialties that are easily available in that area. Also, if the user is traveling, it can propose dishes using local specialties from the travel destination. This makes it possible to provide menus that utilize local specialties.

[0116] The generation unit can estimate the user's emotions and adjust the difficulty of the menu based on the estimated user's emotions. For example, if the user is relaxed, elaborate dishes can be suggested. If the user is in a hurry, easy-to-prepare dishes can be suggested. Furthermore, if the user is stressed, simple and intuitive dishes can be suggested. In this way, the difficulty of the menu can be adjusted according to the user's emotions.

[0117] The analysis unit can suggest ingredient combinations based on the user's past meal history. For example, it can suggest new dishes based on ingredient combinations that the user liked in the past. It can also suggest appropriate dishes based on ingredient combinations that the user avoided in the past. In this way, it is possible to suggest ingredient combinations based on the user's past meal history.

[0118] The providing unit can estimate the user's emotions and adjust the menu providing method based on the estimated user's emotions. For example, if the user is relaxed, a menu with detailed explanations can be provided. If the user is in a hurry, a concise menu that focuses on the main points can be provided. Furthermore, if the user is stressed, a visually appealing menu can be provided. In this way, the menu providing method can be adjusted according to the user's emotions.

[0119] The generation unit can suggest menus that are fortified with specific nutrients based on the user's health data. For example, if the user is iron deficient, it can suggest dishes that use ingredients that are high in iron. Also, if the user wants to take in a lot of vitamin C, it can suggest dishes that are high in that nutrient. In this way, it is possible to suggest menus that are fortified with specific nutrients based on the user's health data.

[0120] The providing unit can estimate the user's emotions and adjust the order in which the menus are provided based on the estimated user's emotions. For example, if the user is relaxed, a menu with detailed explanations can be provided first. If the user is in a hurry, a concise menu that focuses on the main points can be provided first. Furthermore, if the user is stressed, a visually appealing menu can be provided first. In this way, the order in which the menus are provided can be adjusted according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit accepts information entered by the user. The information entered by the user includes, for example, ingredients, preferences, and allergy information. The reception unit allows the user to enter information using a smartphone app, and can also support multiple input methods, such as voice input, text input, and image input. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is carried out using machine learning algorithms and data mining technology, taking into account nutritional balance, cooking time, seasonal ingredients, etc. based on the information entered by the user. Step 3: The generation unit generates a menu based on the information analyzed by the analysis unit. The menu is generated taking into consideration nutritional balance, cooking time, seasonal ingredients, etc., and can also generate a menu taking into consideration the user's preferences and allergy information. Step 4: The provision unit provides the menu generated by the generation unit. This can be done by notifying the user on their smartphone or by displaying it within the app, and can also notify the user that the generated menu takes into consideration the user's preferences and nutritional balance.

[0123] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information; an analysis unit that analyzes the information received by the reception unit; A generation unit that generates a menu based on the information analyzed by the analysis unit; A serving unit that serves the menu generated by the generating unit; Equipped with A system characterized by:

2. The reception unit The user enters information using a smartphone app 2. The system of claim 1.

3. The analysis unit Analyze user input using machine learning 2. The system of claim 1.

4. The generation unit Generate menus based on nutritional balance, cooking time, and seasonal ingredients 2. The system of claim 1.

5. The providing unit The generated menu is sent to the user's smartphone.

2. The system of claim 1.

6. The providing unit Display the generated menu in the app 2. The system of claim 1.

7. The generation unit Generate menus based on user preferences and allergy information 2. The system of claim 1.

8. The providing unit Informs you that the generated menu is based on your preferences and nutritional balance 2. The system of claim 1.

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