System

Abstract

To provide a system for supporting a busy housewife to efficiently think about a menu.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives information on a user. The analysis unit analyzes the information received by the reception unit. The generation part generates a menu on the basis of the information analyzed by the analysis part. The provision part provides the menu generated by the generation part.SELECTED DRAWING: Figure 1

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 has the problem that it is difficult for busy housewives to efficiently plan menus.

[0005] The system according to the embodiment aims to support busy housewives in efficiently planning 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 user 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 assist busy housewives in efficiently planning 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 suggestion system according to an embodiment of the present invention accepts user information, analyzes it with a generation AI, proposes an optimal menu, and generates a list of necessary ingredients. The user inputs information such as ingredients, cooking time, and family preferences, and the generation AI analyzes this information to propose an optimal menu. This proposal also includes specific recipes and cooking procedures. Furthermore, a list of necessary ingredients is generated based on the proposed menu, making shopping easier for the user. For example, the menu suggestion system inputs information such as the ingredients in the refrigerator, the cooking time available, and family allergies and preferences. For example, the user inputs information such as "I have chicken, carrots, and potatoes," "I want to cook it in under 30 minutes," and "My child doesn't like spicy food." The generation AI then analyzes the input information and proposes an optimal menu. The generation AI generates a balanced menu taking into account the input ingredients, cooking time, and family preferences. For example, the system suggests menus such as "stir-fried chicken and vegetables" and "potato soup." This proposal also includes specific recipes and cooking procedures. Furthermore, a list of necessary ingredients is generated based on the proposed menu. The AI ​​generates a list of ingredients needed for the proposed menu, allowing users to easily shop for them. For example, it generates a list such as "chicken, carrots, potatoes, onions, and soup base." This allows the menu suggestion system to efficiently accept and analyze user information, generate, and deliver optimal menus. For example, users simply input the ingredients in their refrigerator, cooking time, and family preferences, and the AI ​​proposes optimal menus and generates a list of necessary ingredients, making meal preparation hassle-free. Specific recipes and cooking instructions are also provided, allowing even novice cooks to cook with confidence. Furthermore, the system also includes features such as the ability to remember users' past input information and use it for future suggestions, suggestions that take nutritional balance into account, the ability to accept user feedback, and the ability to order ingredients online. This allows for more personalized suggestions and improves user convenience.

[0029] A menu suggestion system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user information. The user information includes, but is not limited to, ingredients, cooking time, and family preferences. The reception unit allows the user to input, for example, the ingredients in the refrigerator, the cooking time available, and family allergies and preferences. The analysis unit analyzes the information received by the reception unit. The analysis unit generates a menu, for example, taking nutritional balance into consideration. The analysis unit evaluates the balance of calories, vitamins, and minerals, and proposes an optimal menu. The generation unit uses a generation AI to generate a menu based on the information analyzed by the analysis unit. The generation unit generates a menu including, for example, specific recipes and cooking steps. For example, the generation AI proposes menus such as "stir-fried chicken and vegetables" and "potato soup." The provision unit provides the menu generated by the generation unit. The provision unit provides the generated menu to the user and generates a list of necessary ingredients, for example. The providing unit generates a list such as, for example, "chicken, carrots, potatoes, onions, and soup bases." This allows the menu suggestion system according to the embodiment to efficiently accept and analyze user information, and generate and provide an optimal menu.

[0030] The reception unit can receive information on the user's ingredients, cooking time, and family preferences. For example, the reception unit allows the user to input the ingredients they have in their refrigerator. For example, they can input "I have chicken, carrots, and potatoes." The reception unit can also input the amount of time the user can spend cooking. For example, they can input "I want to cook it in 30 minutes or less." The reception unit can also allow the user to input the family's preferences. For example, they can input "My child doesn't like spicy food." By receiving detailed information from the user, a more personalized menu can be generated.

[0031] The analysis unit can analyze the information received by the reception unit and generate a menu based on nutritional balance. The analysis unit, for example, evaluates the balance of calories, vitamins, and minerals and proposes an optimal menu. For example, the analysis unit can analyze the nutritional value of ingredients input by the user and propose a balanced menu. The analysis unit can also analyze the calorie information of ingredients input by the user and propose a menu that takes into account appropriate calorie intake. The analysis unit can also comprehensively analyze the nutritional value and calorie information of ingredients input by the user and propose a healthy menu. This makes it possible to provide a healthy meal by generating a menu that takes nutritional balance into account.

[0032] The generation unit can use the generation AI to generate menus that include specific recipes and cooking steps. For example, the generation AI may suggest menu items such as "stir-fried chicken and vegetables" or "potato soup." The generation unit can generate menus that include specific recipes and cooking steps. For example, the generation AI may receive a prompt such as "Please suggest a dish that can be made in 30 minutes or less using these ingredients," and generate specific recipes and cooking steps. The generation unit can also generate menus that take into account the user's preferences and allergy information. For example, the generation AI may receive a prompt such as "My child doesn't like spicy food, so please suggest a non-spicy dish," and generate an appropriate menu. This allows users to easily cook by generating menus that include specific recipes and cooking steps.

[0033] The providing unit can provide the generated menu to the user and generate a list of necessary ingredients. For example, the providing unit provides the generated menu to the user and generates a list of necessary ingredients. For example, the providing unit generates a list such as "chicken, carrots, potatoes, onions, and soup base." The providing unit can also display the nutritional balance of the generated menu. For example, the providing unit displays the balance of calories, vitamins, and minerals of the generated menu. In this way, generating a list of necessary ingredients allows the user to easily shop.

[0034] The memory unit can store information previously input by the user and use it for the next suggestion. The memory unit, for example, stores information previously input by the user and uses it for the next suggestion. For example, the memory unit stores information previously input by the user, such as ingredients, cooking time, and family preferences. The memory unit can also store the user's past menu history and use it for the next suggestion. For example, the memory unit can store menus previously made by the user and suggest new menus so that the same dishes are not duplicated. In this way, by storing previously input information, the next suggestion can be more personalized.

[0035] The display unit can display the nutritional balance of the generated menu. The display unit displays, for example, the balance of calories, vitamins, and minerals of the generated menu. For example, the display unit displays the nutritional balance of the generated menu as a graph or chart. The display unit can also display the nutritional balance of the generated menu in text format. For example, the display unit displays the balance of calories, vitamins, and minerals of the generated menu as numerical values. This allows the user to select a healthy diet by displaying the nutritional balance.

[0036] The feedback unit can accept user feedback and provide the feedback to the generation unit. For example, the feedback unit accepts user feedback and provides the feedback to the generation unit. For example, the feedback unit provides feedback to the generation unit based on the feedback provided by the user. The feedback unit can also adjust the algorithm of the generation unit based on the user feedback. For example, the feedback unit optimizes the algorithm of the generation unit based on the feedback provided by the user. This improves the accuracy of the system by reflecting the user feedback.

[0037] The ordering unit can enable the user to order ingredients online based on the proposed menu. The ordering unit, for example, enables the user to order ingredients online based on the proposed menu. For example, the ordering unit enables the user to order ingredients needed for the proposed menu online. The ordering unit can also display a list of ingredients ordered online by the user. For example, the ordering unit displays a list of ingredients ordered online by the user, allowing the user to confirm the order details. This allows the user to order ingredients online, improving convenience.

[0038] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit can, for example, analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display ingredients and cooking times that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and cooking times to be used in a specific time period based on the user's past input history. In this way, an interface that is easy for the user to use can be provided by analyzing the past input history.

[0039] The reception unit can filter the input information based on the freshness and expiration date of the ingredients. For example, the reception unit automatically checks the expiration date of the ingredients input by the user and prioritizes suggesting ingredients with an approaching expiration date. The reception unit can also suggest the optimal cooking method based on the freshness information of the ingredients input by the user. The reception unit can also filter usable ingredients taking into account the storage conditions of the ingredients input by the user. This makes it possible to suggest a menu that does not waste food by taking into account the freshness and expiration date of the ingredients.

[0040] The reception unit can analyze the user's voice input and receive information using natural language processing. For example, the reception unit can automatically set ingredient information when the user simply inputs "a dish using chicken and carrots" by voice. The reception unit can also automatically set cooking time when the user simply inputs "a dish that can be made in 30 minutes or less" by voice. The reception unit can also automatically set family preferences when the user simply inputs "my child doesn't like spicy food" by voice. This allows the user to easily input information by analyzing voice input.

[0041] The analysis unit can analyze the nutritional value and calorie information of ingredients to generate a balanced menu. For example, the analysis unit can analyze the nutritional value of ingredients input by the user and propose a balanced menu. The analysis unit can also analyze the calorie information of ingredients input by the user and propose a menu that takes into account an appropriate calorie intake. The analysis unit can also comprehensively analyze the nutritional value and calorie information of ingredients input by the user and propose a healthy menu. In this way, a healthy menu can be proposed by analyzing the nutritional value and calorie information.

[0042] The analysis unit can suggest menus that match the user's preferences by referring to the user's past meal history. The analysis unit can suggest menus that match the user's preferences, for example, based on dishes that the user has liked to eat in the past. The analysis unit can also suggest menus that use specific ingredients from the user's past meal history. The analysis unit can also analyze the user's past meal history and suggest new menus that match the user's preferences. In this way, by referring to the user's past meal history, it is possible to suggest menus that match the user's preferences.

[0043] The analysis unit can analyze the optimal menu depending on the season and weather. For example, the analysis unit can propose a menu using ingredients that are in season. The analysis unit can also propose cooking methods (hot dishes, cold dishes, etc.) that are in accordance with the weather. The analysis unit can also propose a menu that takes into account the nutritional balance that is in accordance with the season and weather. In this way, by proposing a menu that is in accordance with the season and weather, user satisfaction is improved.

[0044] The generation unit can optimize ingredient combinations and cooking methods when generating a menu. For example, the generation unit optimizes ingredient combinations input by the user to generate a balanced menu. The generation unit can also suggest cooking methods that are optimal for the ingredients input by the user to generate an efficient menu. The generation unit can also comprehensively optimize ingredient combinations and cooking methods input by the user to generate a healthy menu. In this way, an efficient menu can be generated by optimizing ingredient combinations and cooking methods.

[0045] The generation unit can refer to the user's past menu history to generate a menu that does not overlap. For example, the generation unit can refer to menus that the user has made in the past and generate a new menu that does not include duplicate dishes. The generation unit can also analyze the user's past menu history to suggest menus with a wide variety. The generation unit can also take into account the frequency with which the user has made dishes in the past and preferentially suggest dishes that are not made frequently. In this way, by referring to the past menu history, a wide variety of menus can be suggested.

[0046] The generation unit can give priority to the use of specific ingredients and cooking utensils when generating a menu. For example, the generation unit generates a menu that gives priority to the use of specific ingredients input by the user. The generation unit can also generate a menu that uses specific cooking utensils (e.g., pressure cooker, frying pan) that the user owns. The generation unit can also generate an optimal menu by taking into account specific ingredients and cooking utensils that the user prefers. In this way, a menu that suits the user's preferences can be generated by giving priority to the use of specific ingredients and cooking utensils.

[0047] When providing a menu, the providing unit can select the optimal serving timing by taking into account the user's past preferences. The providing unit considers, for example, the serving timing of dishes that the user has liked to eat in the past. The providing unit can also suggest the optimal serving timing based on the user's past preferences. The providing unit can also analyze the user's past preferences and select the most efficient serving timing. In this way, by taking into account the user's past preferences, the menu can be provided at the optimal timing for the user.

[0048] When providing a menu, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by taking into account the device information, a display method that is easy for the user to see can be provided.

[0049] The providing unit can improve the serving method by reflecting the user's feedback when providing a menu. For example, the providing unit adjusts the serving method based on feedback previously provided by the user. The providing unit can also improve the accuracy of the serving method based on feedback previously provided by the user. The providing unit can also optimize the parameters of the serving method based on feedback previously provided by the user. In this way, the accuracy of the serving method is improved by reflecting the feedback.

[0050] The storage unit can select the optimal storage method by referring to past data when storing data. The storage unit selects the optimal storage method based on data stored by the user in the past, for example. The storage unit can also analyze the user's past data and select an efficient storage method. The storage unit can also select the most appropriate storage method by referring to the user's past data. In this way, an efficient storage method can be selected by referring to past data.

[0051] The storage unit can update the stored data by reflecting user feedback when storing the data. The storage unit updates the stored data, for example, based on feedback provided by the user. The storage unit can also reflect user feedback to improve the accuracy of the stored data. The storage unit can also optimize parameters of the stored data based on user feedback. In this way, the accuracy of the stored data is improved by reflecting feedback.

[0052] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit provides the optimal display method based on, for example, a display method that the user has previously preferred. The display unit can also analyze the user's past operation history and provide an efficient display method. The display unit can also select the most appropriate display method by referring to the user's past operation history. In this way, by referring to the past operation history, a display method that is easy for the user to use can be provided.

[0053] The display unit can customize the display content according to the user's current task. For example, if the user is cooking, the display unit can prioritize displaying cooking steps. Also, if the user is shopping, the display unit can prioritize displaying a list of necessary ingredients. Also, if the user is selecting a menu, the display unit can prioritize displaying a balanced menu. This improves user convenience by providing display content according to the user's current task.

[0054] When receiving feedback, the feedback unit can select the optimal receiving method by referring to the user's past feedback history. The feedback unit selects the optimal receiving method, for example, based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback history and select an efficient receiving method. The feedback unit can also select the most appropriate receiving method by referring to the user's past feedback history. In this way, an efficient feedback receiving method can be provided by referring to the past feedback history.

[0055] When receiving feedback, the feedback unit can customize the received content according to the user's current situation. For example, if the user is cooking, the feedback unit can prioritize receiving feedback about cooking procedures. Also, if the user is shopping, the feedback unit can prioritize receiving feedback about an ingredient list. Also, if the user is selecting a menu, the feedback unit can prioritize receiving feedback about the menu. This improves user convenience by providing feedback received content according to the current situation.

[0056] When placing an order, the ordering unit can refer to the user's past order history to select the most appropriate ordering method. The ordering unit selects the most appropriate ordering method, for example, based on products the user has ordered in the past. The ordering unit can also analyze the user's past order history to select an efficient ordering method. The ordering unit can also refer to the user's past order history to select the most appropriate ordering method. In this way, by referring to the past order history, an efficient ordering method can be provided.

[0057] The ordering unit can customize the order contents according to the user's current situation when placing an order. For example, if the user is cooking, the ordering unit will prioritize ordering the necessary ingredients. Also, if the user is shopping, the ordering unit can prioritize ordering ingredients that the user forgot to buy. Also, if the user is selecting a menu, the ordering unit can prioritize ordering ingredients that are necessary for the menu. This improves user convenience by providing order contents according to the user's current situation.

[0058] When placing an order, the ordering unit can accept region-specific orders by taking into account the user's geographical location information. For example, the ordering unit can prioritize ordering local specialties of the region where the user lives. The ordering unit can also prioritize ordering seasonal ingredients of the region where the user lives. The ordering unit can also prioritize ordering ingredients that are available at supermarkets in the region where the user lives. In this way, region-specific orders can be accepted by taking into account the geographical location information.

[0059] The ordering unit can analyze the user's social media activity at the time of ordering and accept related orders. For example, the ordering unit automatically orders the necessary ingredients based on a recipe shared by the user on social media. The ordering unit can also analyze the posts of the user's friends on social media and order related ingredients. The ordering unit can also analyze the content of the user's social media posts and order new related ingredients. In this way, related orders can be efficiently accepted by analyzing social media activity.

[0060] The storage unit can store region-specific data taking into account the user's geographical location information. For example, the storage unit stores data related to local specialties of the region where the user lives. The storage unit can also store data related to seasonal ingredients of the region where the user lives. The storage unit can also store data related to traditional dishes of the region where the user lives. This allows region-specific data to be stored efficiently by taking into account the geographical location information.

[0061] When storing data, the storage unit can analyze the user's social media activity and store related data. For example, the storage unit stores data related to recipes shared by the user on social media. The storage unit can also analyze posts by the user's friends on social media and store related data. The storage unit can also analyze the content of the user's posts on social media and store new related data. This makes it possible to efficiently store related data by analyzing social media activity.

[0062] The display unit can select the optimal display method when displaying information by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, by taking into account the device information, it is possible to provide a display method that is easy for the user to see.

[0063] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide multilingual support by providing display content according to the language setting.

[0064] When receiving feedback, the feedback unit can receive region-specific feedback by taking into account the user's geographical location information. For example, the feedback unit can receive feedback regarding local specialties in the region where the user lives. The feedback unit can also receive feedback regarding seasonal ingredients in the region where the user lives. The feedback unit can also receive feedback regarding traditional dishes in the region where the user lives. In this way, region-specific feedback can be efficiently received by taking into account the geographical location information.

[0065] When receiving feedback, the feedback unit can analyze the user's social media activity and receive related feedback. For example, the feedback unit can receive feedback regarding recipes shared by the user on social media. The feedback unit can also analyze posts by the user's friends on social media and receive related feedback. The feedback unit can also analyze the content of the user's posts on social media and receive new related feedback. In this way, by analyzing social media activity, related feedback can be efficiently received.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The analysis unit can suggest menus that match the user's preferences by referring to the user's past meal history. For example, the analysis unit can suggest menus that match the user's preferences based on dishes that the user has liked to eat in the past. The analysis unit can also suggest menus that use specific ingredients based on the user's past meal history. The analysis unit can also analyze the user's past meal history and suggest new menus that match the user's preferences. In this way, by referring to the user's past meal history, it is possible to suggest menus that match the user's preferences.

[0068] The analysis unit can analyze the optimal menu depending on the season and weather. For example, the analysis unit can propose a menu using ingredients that are in season. The analysis unit can also propose cooking methods (hot dishes, cold dishes, etc.) that are in accordance with the weather. The analysis unit can also propose a menu that takes into account the nutritional balance that is in accordance with the season and weather. In this way, by proposing a menu that is in accordance with the season and weather, user satisfaction is improved.

[0069] The generation unit can optimize the combination of ingredients and cooking methods when generating a menu. For example, the generation unit can optimize the combination of ingredients input by the user to generate a balanced menu. The generation unit can also suggest the optimal cooking method for the ingredients input by the user to generate an efficient menu. The generation unit can also comprehensively optimize the combination of ingredients and cooking methods input by the user to generate a healthy menu. In this way, an efficient menu can be generated by optimizing the combination of ingredients and cooking methods.

[0070] The generation unit can refer to the user's past menu history to generate a menu that does not overlap. For example, the generation unit can refer to menus the user has made in the past and generate a new menu that does not include duplicate dishes. The generation unit can also analyze the user's past menu history to suggest menus with a wide variety. The generation unit can also take into account the frequency with which the user has made dishes in the past and preferentially suggest dishes that are not made frequently. In this way, by referring to the past menu history, a wide variety of menus can be suggested.

[0071] The generation unit can give priority to the use of specific ingredients and cooking utensils when generating a menu. For example, the generation unit generates a menu that gives priority to the use of specific ingredients input by the user. The generation unit can also generate a menu that uses specific cooking utensils (e.g., pressure cooker, frying pan) that the user owns. The generation unit can also generate an optimal menu by taking into account specific ingredients and cooking utensils that the user prefers. In this way, a menu that suits the user's preferences can be generated by giving priority to the use of specific ingredients and cooking utensils.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives user information. User information includes, for example, ingredients, cooking time, and family preferences. Users can input the ingredients they have in their refrigerator, the time they can spend cooking, and their family's allergies and preferences. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit evaluates the balance of calories, vitamins, and minerals to generate a menu that takes nutritional balance into consideration. Step 3: The generation unit generates a menu based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a menu that includes specific recipes and cooking procedures, and suggests dishes such as "stir-fried chicken and vegetables" or "potato soup." Step 4: The providing unit provides the menu generated by the generating unit. The providing unit provides the generated menu to the user and generates a list of necessary ingredients. For example, a list such as "chicken, carrots, potatoes, onions, and soup base" is generated.

[0074] (Example 2) A menu suggestion system according to an embodiment of the present invention accepts user information, analyzes it with a generation AI, proposes an optimal menu, and generates a list of necessary ingredients. The user inputs information such as ingredients, cooking time, and family preferences, and the generation AI analyzes this information to propose an optimal menu. This proposal also includes specific recipes and cooking procedures. Furthermore, a list of necessary ingredients is generated based on the proposed menu, making shopping easier for the user. For example, the menu suggestion system inputs information such as the ingredients in the refrigerator, the cooking time available, and family allergies and preferences. For example, the user inputs information such as "I have chicken, carrots, and potatoes," "I want to cook it in under 30 minutes," and "My child doesn't like spicy food." The generation AI then analyzes the input information and proposes an optimal menu. The generation AI generates a balanced menu taking into account the input ingredients, cooking time, and family preferences. For example, the system suggests menus such as "stir-fried chicken and vegetables" and "potato soup." This proposal also includes specific recipes and cooking procedures. Furthermore, a list of necessary ingredients is generated based on the proposed menu. The AI ​​generates a list of ingredients needed for the proposed menu, allowing users to easily shop for them. For example, it generates a list such as "chicken, carrots, potatoes, onions, and soup base." This allows the menu suggestion system to efficiently accept and analyze user information, generate, and deliver optimal menus. For example, users simply input the ingredients in their refrigerator, cooking time, and family preferences, and the AI ​​proposes optimal menus and generates a list of necessary ingredients, making meal preparation hassle-free. Specific recipes and cooking instructions are also provided, allowing even novice cooks to cook with confidence. Furthermore, the system also includes features such as the ability to remember users' past input information and use it for future suggestions, suggestions that take nutritional balance into account, the ability to accept user feedback, and the ability to order ingredients online. This allows for more personalized suggestions and improves user convenience.

[0075] A menu suggestion system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user information. The user information includes, but is not limited to, ingredients, cooking time, and family preferences. The reception unit allows the user to input, for example, the ingredients in the refrigerator, the cooking time available, and family allergies and preferences. The analysis unit analyzes the information received by the reception unit. The analysis unit generates a menu, for example, taking nutritional balance into consideration. The analysis unit evaluates the balance of calories, vitamins, and minerals, and proposes an optimal menu. The generation unit uses a generation AI to generate a menu based on the information analyzed by the analysis unit. The generation unit generates a menu including, for example, specific recipes and cooking steps. For example, the generation AI proposes menus such as "stir-fried chicken and vegetables" and "potato soup." The provision unit provides the menu generated by the generation unit. The provision unit provides the generated menu to the user and generates a list of necessary ingredients, for example. The providing unit generates a list such as, for example, "chicken, carrots, potatoes, onions, and soup bases." This allows the menu suggestion system according to the embodiment to efficiently accept and analyze user information, and generate and provide an optimal menu.

[0076] The reception unit can receive information on the user's ingredients, cooking time, and family preferences. For example, the reception unit allows the user to input the ingredients they have in their refrigerator. For example, they can input "I have chicken, carrots, and potatoes." The reception unit can also input the amount of time the user can spend cooking. For example, they can input "I want to cook it in 30 minutes or less." The reception unit can also allow the user to input the family's preferences. For example, they can input "My child doesn't like spicy food." By receiving detailed information from the user, a more personalized menu can be generated.

[0077] The analysis unit can analyze the information received by the reception unit and generate a menu based on nutritional balance. The analysis unit, for example, evaluates the balance of calories, vitamins, and minerals and proposes an optimal menu. For example, the analysis unit can analyze the nutritional value of ingredients input by the user and propose a balanced menu. The analysis unit can also analyze the calorie information of ingredients input by the user and propose a menu that takes into account appropriate calorie intake. The analysis unit can also comprehensively analyze the nutritional value and calorie information of ingredients input by the user and propose a healthy menu. This makes it possible to provide a healthy meal by generating a menu that takes nutritional balance into account.

[0078] The generation unit can use the generation AI to generate menus that include specific recipes and cooking steps. For example, the generation AI may suggest menu items such as "stir-fried chicken and vegetables" or "potato soup." The generation unit can generate menus that include specific recipes and cooking steps. For example, the generation AI may receive a prompt such as "Please suggest a dish that can be made in 30 minutes or less using these ingredients," and generate specific recipes and cooking steps. The generation unit can also generate menus that take into account the user's preferences and allergy information. For example, the generation AI may receive a prompt such as "My child doesn't like spicy food, so please suggest a non-spicy dish," and generate an appropriate menu. This allows users to easily cook by generating menus that include specific recipes and cooking steps.

[0079] The providing unit can provide the generated menu to the user and generate a list of necessary ingredients. For example, the providing unit provides the generated menu to the user and generates a list of necessary ingredients. For example, the providing unit generates a list such as "chicken, carrots, potatoes, onions, and soup base." The providing unit can also display the nutritional balance of the generated menu. For example, the providing unit displays the balance of calories, vitamins, and minerals of the generated menu. In this way, generating a list of necessary ingredients allows the user to easily shop.

[0080] The memory unit can store information previously input by the user and use it for the next suggestion. The memory unit, for example, stores information previously input by the user and uses it for the next suggestion. For example, the memory unit stores information previously input by the user, such as ingredients, cooking time, and family preferences. The memory unit can also store the user's past menu history and use it for the next suggestion. For example, the memory unit can store menus previously made by the user and suggest new menus so that the same dishes are not duplicated. In this way, by storing previously input information, the next suggestion can be more personalized.

[0081] The display unit can display the nutritional balance of the generated menu. The display unit displays, for example, the balance of calories, vitamins, and minerals of the generated menu. For example, the display unit displays the nutritional balance of the generated menu as a graph or chart. The display unit can also display the nutritional balance of the generated menu in text format. For example, the display unit displays the balance of calories, vitamins, and minerals of the generated menu as numerical values. This allows the user to select a healthy diet by displaying the nutritional balance.

[0082] The feedback unit can accept user feedback and provide the feedback to the generation unit. For example, the feedback unit accepts user feedback and provides the feedback to the generation unit. For example, the feedback unit provides feedback to the generation unit based on the feedback provided by the user. The feedback unit can also adjust the algorithm of the generation unit based on the user feedback. For example, the feedback unit optimizes the algorithm of the generation unit based on the feedback provided by the user. This improves the accuracy of the system by reflecting the user feedback.

[0083] The ordering unit can enable the user to order ingredients online based on the proposed menu. The ordering unit, for example, enables the user to order ingredients online based on the proposed menu. For example, the ordering unit enables the user to order ingredients needed for the proposed menu online. The ordering unit can also display a list of ingredients ordered online by the user. For example, the ordering unit displays a list of ingredients ordered online by the user, allowing the user to confirm the order details. This allows the user to order ingredients online, improving convenience.

[0084] The reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This can reduce user stress by providing an input method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit can, for example, analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display ingredients and cooking times that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and cooking times to be used in a specific time period based on the user's past input history. In this way, an interface that is easy for the user to use can be provided by analyzing the past input history.

[0086] The reception unit can filter the input information based on the freshness and expiration date of the ingredients. For example, the reception unit automatically checks the expiration date of the ingredients input by the user and prioritizes suggesting ingredients with an approaching expiration date. The reception unit can also suggest the optimal cooking method based on the freshness information of the ingredients input by the user. The reception unit can also filter usable ingredients taking into account the storage conditions of the ingredients input by the user. This makes it possible to suggest a menu that does not waste food by taking into account the freshness and expiration date of the ingredients.

[0087] The reception unit can analyze the user's voice input and receive information using natural language processing. For example, the reception unit can automatically set ingredient information when the user simply inputs "a dish using chicken and carrots" by voice. The reception unit can also automatically set cooking time when the user simply inputs "a dish that can be made in 30 minutes or less" by voice. The reception unit can also automatically set family preferences when the user simply inputs "my child doesn't like spicy food" by voice. This allows the user to easily input information by analyzing voice input.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit uses a simple and quick analysis algorithm. Alternatively, if the user is relaxed, the analysis unit can use a detailed analysis algorithm. Alternatively, if the user is in a hurry, the analysis unit can use an analysis algorithm that provides results quickly. This improves the accuracy of the analysis results by using an analysis algorithm that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The analysis unit can analyze the nutritional value and calorie information of ingredients to generate a balanced menu. For example, the analysis unit can analyze the nutritional value of ingredients input by the user and propose a balanced menu. The analysis unit can also analyze the calorie information of ingredients input by the user and propose a menu that takes into account an appropriate calorie intake. The analysis unit can also comprehensively analyze the nutritional value and calorie information of ingredients input by the user and propose a healthy menu. In this way, a healthy menu can be proposed by analyzing the nutritional value and calorie information.

[0090] The analysis unit can suggest menus that match the user's preferences by referring to the user's past meal history. The analysis unit can suggest menus that match the user's preferences, for example, based on dishes that the user has liked to eat in the past. The analysis unit can also suggest menus that use specific ingredients from the user's past meal history. The analysis unit can also analyze the user's past meal history and suggest new menus that match the user's preferences. In this way, by referring to the user's past meal history, it is possible to suggest menus that match the user's preferences.

[0091] The analysis unit can analyze the optimal menu depending on the season and weather. For example, the analysis unit can propose a menu using ingredients that are in season. The analysis unit can also propose cooking methods (hot dishes, cold dishes, etc.) that are in accordance with the weather. The analysis unit can also propose a menu that takes into account the nutritional balance that is in accordance with the season and weather. In this way, by proposing a menu that is in accordance with the season and weather, user satisfaction is improved.

[0092] The generation unit can estimate the user's emotions and adjust the presentation method of the menu based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the presentation method of the menu based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a simple and highly visible menu. Furthermore, if the user is relaxed, the generation unit can generate a menu that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a menu that focuses on the main points. This allows the user's stress to be reduced by providing an expression method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The generation unit can optimize ingredient combinations and cooking methods when generating a menu. For example, the generation unit optimizes ingredient combinations input by the user to generate a balanced menu. The generation unit can also suggest cooking methods that are optimal for the ingredients input by the user to generate an efficient menu. The generation unit can also comprehensively optimize ingredient combinations and cooking methods input by the user to generate a healthy menu. In this way, an efficient menu can be generated by optimizing ingredient combinations and cooking methods.

[0094] The generation unit can refer to the user's past menu history to generate a menu that does not overlap. For example, the generation unit can refer to menus that the user has made in the past and generate a new menu that does not include duplicate dishes. The generation unit can also analyze the user's past menu history to suggest menus with a wide variety. The generation unit can also take into account the frequency with which the user has made dishes in the past and preferentially suggest dishes that are not made frequently. In this way, by referring to the past menu history, a wide variety of menus can be suggested.

[0095] The generation unit can give priority to the use of specific ingredients and cooking utensils when generating a menu. For example, the generation unit generates a menu that gives priority to the use of specific ingredients input by the user. The generation unit can also generate a menu that uses specific cooking utensils (e.g., pressure cooker, frying pan) that the user owns. The generation unit can also generate an optimal menu by taking into account specific ingredients and cooking utensils that the user prefers. In this way, a menu that suits the user's preferences can be generated by giving priority to the use of specific ingredients and cooking utensils.

[0096] The provision unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user emotions. The provision unit, for example, estimates the user's emotions and adjusts the menu presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the provision unit uses a simple, highly visible presentation method. If the user is relaxed, the provision unit can also use a presentation method that includes detailed information. If the user is in a hurry, the provision unit can also use a presentation method that focuses on the main points. This allows the user's stress to be reduced by providing a presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] When providing a menu, the providing unit can select the optimal serving timing by taking into account the user's past preferences. The providing unit considers, for example, the serving timing of dishes that the user has liked to eat in the past. The providing unit can also suggest the optimal serving timing based on the user's past preferences. The providing unit can also analyze the user's past preferences and select the most efficient serving timing. In this way, by taking into account the user's past preferences, the menu can be provided at the optimal timing for the user.

[0098] When providing a menu, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by taking into account the device information, a display method that is easy for the user to see can be provided.

[0099] The providing unit can improve the serving method by reflecting the user's feedback when providing a menu. For example, the providing unit adjusts the serving method based on feedback previously provided by the user. The providing unit can also improve the accuracy of the serving method based on feedback previously provided by the user. The providing unit can also optimize the parameters of the serving method based on feedback previously provided by the user. In this way, the accuracy of the serving method is improved by reflecting the feedback.

[0100] The memory unit can estimate the user's emotions and select stored data based on the estimated user emotions. The memory unit, for example, estimates the user's emotions and selects stored data based on the estimated user emotions. For example, when the user is feeling stressed, the memory unit prioritizes storing important data. Furthermore, when the user is relaxed, the memory unit can prioritize storing detailed data. Furthermore, when the user is in a hurry, the memory unit can prioritize storing data that highlights the main points. In this way, by selecting data according to the user's emotions, important data can be prioritized for storage. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The storage unit can select the optimal storage method by referring to past data when storing data. The storage unit selects the optimal storage method based on data stored by the user in the past, for example. The storage unit can also analyze the user's past data and select an efficient storage method. The storage unit can also select the most appropriate storage method by referring to the user's past data. In this way, an efficient storage method can be selected by referring to past data.

[0102] The storage unit can update the stored data by reflecting user feedback when storing the data. The storage unit updates the stored data, for example, based on feedback provided by the user. The storage unit can also reflect user feedback to improve the accuracy of the stored data. The storage unit can also optimize parameters of the stored data based on user feedback. In this way, the accuracy of the stored data is improved by reflecting feedback.

[0103] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can also provide a display method including detailed information. If the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This can reduce the user's stress by providing a display method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit provides the optimal display method based on, for example, a display method that the user has previously preferred. The display unit can also analyze the user's past operation history and provide an efficient display method. The display unit can also select the most appropriate display method by referring to the user's past operation history. In this way, by referring to the past operation history, a display method that is easy for the user to use can be provided.

[0105] The display unit can customize the display content according to the user's current task. For example, if the user is cooking, the display unit can prioritize displaying cooking steps. Also, if the user is shopping, the display unit can prioritize displaying a list of necessary ingredients. Also, if the user is selecting a menu, the display unit can prioritize displaying a balanced menu. This improves user convenience by providing display content according to the user's current task.

[0106] The feedback unit can estimate the user's emotion and adjust the feedback acceptance method based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the feedback acceptance method based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback unit can provide a simple and quick feedback acceptance method. If the user is relaxed, the feedback unit can also provide detailed feedback options. If the user is in a hurry, the feedback unit can prioritize voice input and quickly accept feedback. This can reduce the user's stress by providing a feedback acceptance method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When receiving feedback, the feedback unit can select the optimal receiving method by referring to the user's past feedback history. The feedback unit selects the optimal receiving method, for example, based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback history and select an efficient receiving method. The feedback unit can also select the most appropriate receiving method by referring to the user's past feedback history. In this way, an efficient feedback receiving method can be provided by referring to the past feedback history.

[0108] When receiving feedback, the feedback unit can customize the received content according to the user's current situation. For example, if the user is cooking, the feedback unit can prioritize receiving feedback about cooking procedures. Also, if the user is shopping, the feedback unit can prioritize receiving feedback about an ingredient list. Also, if the user is selecting a menu, the feedback unit can prioritize receiving feedback about the menu. This improves user convenience by providing feedback received content according to the current situation.

[0109] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated user emotions. For example, the ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated user emotions. For example, if the user is feeling stressed, the ordering unit can provide a simple and quick ordering method. Also, if the user is relaxed, the ordering unit can provide detailed ordering options. Also, if the user is in a hurry, the ordering unit can prioritize voice input and enable quick ordering. This can reduce the user's stress by providing an ordering method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] When placing an order, the ordering unit can refer to the user's past order history to select the most appropriate ordering method. The ordering unit selects the most appropriate ordering method, for example, based on products the user has ordered in the past. The ordering unit can also analyze the user's past order history to select an efficient ordering method. The ordering unit can also refer to the user's past order history to select the most appropriate ordering method. In this way, by referring to the past order history, an efficient ordering method can be provided.

[0111] The ordering unit can customize the order contents according to the user's current situation when placing an order. For example, if the user is cooking, the ordering unit will prioritize ordering the necessary ingredients. Also, if the user is shopping, the ordering unit can prioritize ordering ingredients that the user forgot to buy. Also, if the user is selecting a menu, the ordering unit can prioritize ordering ingredients that are necessary for the menu. This improves user convenience by providing order contents according to the user's current situation.

[0112] The order unit can estimate the user's emotions and determine the priority of orders based on the estimated user emotions. The order unit, for example, estimates the user's emotions and determines the priority of orders based on the estimated user emotions. For example, if the user is feeling stressed, the order unit can prioritize important orders. Also, if the user is relaxed, the order unit can prioritize detailed orders. Also, if the user is in a hurry, the order unit can prioritize orders that focus on the main points. In this way, by providing priorities according to the user's emotions, important orders can be prioritized. Emotion estimation is realized using an emotion estimation function using, 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.

[0113] When placing an order, the ordering unit can accept region-specific orders by taking into account the user's geographical location information. For example, the ordering unit can prioritize ordering local specialties of the region where the user lives. The ordering unit can also prioritize ordering seasonal ingredients of the region where the user lives. The ordering unit can also prioritize ordering ingredients that are available at supermarkets in the region where the user lives. In this way, region-specific orders can be accepted by taking into account the geographical location information.

[0114] The ordering unit can analyze the user's social media activity at the time of ordering and accept related orders. For example, the ordering unit automatically orders the necessary ingredients based on a recipe shared by the user on social media. The ordering unit can also analyze the posts of the user's friends on social media and order related ingredients. The ordering unit can also analyze the content of the user's social media posts and order new related ingredients. In this way, related orders can be efficiently accepted by analyzing social media activity.

[0115] The storage unit can store region-specific data taking into account the user's geographical location information. For example, the storage unit stores data related to local specialties of the region where the user lives. The storage unit can also store data related to seasonal ingredients of the region where the user lives. The storage unit can also store data related to traditional dishes of the region where the user lives. This allows region-specific data to be stored efficiently by taking into account the geographical location information.

[0116] When storing data, the storage unit can analyze the user's social media activity and store related data. For example, the storage unit stores data related to recipes shared by the user on social media. The storage unit can also analyze posts by the user's friends on social media and store related data. The storage unit can also analyze the content of the user's posts on social media and store new related data. This makes it possible to efficiently store related data by analyzing social media activity.

[0117] The display unit can estimate the user's emotion and determine the priority of display content based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and determines the priority of display content based on the estimated user's emotion. For example, when the user is feeling stressed, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that focuses on the main points. In this way, by providing a priority order of display content according to the user's emotion, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The display unit can select the optimal display method when displaying information by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, by taking into account the device information, it is possible to provide a display method that is easy for the user to see.

[0119] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide multilingual support by providing display content according to the language setting.

[0120] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, the feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can prioritize receiving important feedback. Also, if the user is relaxed, the feedback unit can prioritize receiving detailed feedback. Also, if the user is in a hurry, the feedback unit can prioritize receiving feedback that focuses on the main points. In this way, by providing feedback priorities according to the user's emotions, important feedback can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] When receiving feedback, the feedback unit can receive region-specific feedback by taking into account the user's geographical location information. For example, the feedback unit can receive feedback regarding local specialties in the region where the user lives. The feedback unit can also receive feedback regarding seasonal ingredients in the region where the user lives. The feedback unit can also receive feedback regarding traditional dishes in the region where the user lives. In this way, region-specific feedback can be efficiently received by taking into account the geographical location information.

[0122] When receiving feedback, the feedback unit can analyze the user's social media activity and receive related feedback. For example, the feedback unit can receive feedback regarding recipes shared by the user on social media. The feedback unit can also analyze posts by the user's friends on social media and receive related feedback. The feedback unit can also analyze the content of the user's posts on social media and receive new related feedback. In this way, by analyzing social media activity, related feedback can be efficiently received. === 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 allows the user to input information such as ingredients in the refrigerator, the cooking time available, and family allergies and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and generates a menu taking nutritional balance into consideration. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu including specific recipes and cooking procedures using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated menu and a list of necessary ingredients to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and provision unit, described above, 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, and allows the user to voice-input information such as ingredients in the refrigerator, cooking time, and family allergies and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and generates a menu taking nutritional balance into consideration. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu including specific recipes and cooking procedures using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated menu and a list of necessary ingredients to the user via 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 terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, allowing the user to voice-input information such as ingredients in the refrigerator, the cooking time available, and family allergies and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and generates a menu taking nutritional balance into consideration. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to generate a menu including specific recipes and cooking procedures. The provision unit is realized, for example, by the display 343 of the headset terminal 314, and visually presents the generated menu and a list of necessary ingredients to the user. === 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 allows the user to voice-input information such as ingredients in the refrigerator, the cooking time available, and family allergies and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and generates a menu taking nutritional balance into consideration. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a menu including specific recipes and cooking procedures using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated menu and a list of necessary ingredients to the user via voice.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit can use a simple and quick analysis algorithm. If the user is relaxed, the analysis unit can also use a detailed analysis algorithm. If the user is in a hurry, the analysis unit can also use an analysis algorithm that provides results quickly. This improves the accuracy of the analysis results by using an analysis algorithm that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0125] The providing unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit uses a simple, highly visible presentation method. If the user is relaxed, the providing unit can also use a presentation method that includes detailed information. If the user is in a hurry, the providing unit can also use a presentation method that focuses on the main points. This allows the user's stress to be reduced by providing a presentation method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0126] The memory unit can estimate the user's emotions and select stored data based on the estimated user emotions. For example, if the user is feeling stressed, the memory unit can prioritize storing important data. Also, if the user is relaxed, the memory unit can prioritize storing detailed data. Also, if the user is in a hurry, the memory unit can prioritize storing data that covers the main points. In this way, by selecting data according to the user's emotions, important data can be prioritized for storage. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The feedback unit can estimate the user's emotions and adjust the feedback acceptance method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit can provide a simple and quick feedback acceptance method. If the user is relaxed, the feedback unit can also provide detailed feedback options. If the user is in a hurry, the feedback unit can prioritize voice input and accept feedback quickly. This can reduce the user's stress by providing a feedback acceptance method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated user emotions. For example, if the user is feeling stressed, the ordering unit can provide a simple and quick ordering method. If the user is relaxed, the ordering unit can also provide detailed ordering options. If the user is in a hurry, the ordering unit can prioritize voice input and enable quick ordering. This can reduce the user's stress by providing an ordering method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The analysis unit can suggest menus that match the user's preferences by referring to the user's past meal history. For example, the analysis unit can suggest menus that match the user's preferences based on dishes that the user has liked to eat in the past. The analysis unit can also suggest menus that use specific ingredients based on the user's past meal history. The analysis unit can also analyze the user's past meal history and suggest new menus that match the user's preferences. In this way, by referring to the user's past meal history, it is possible to suggest menus that match the user's preferences.

[0130] The analysis unit can analyze the optimal menu depending on the season and weather. For example, the analysis unit can propose a menu using ingredients that are in season. The analysis unit can also propose cooking methods (hot dishes, cold dishes, etc.) that are in accordance with the weather. The analysis unit can also propose a menu that takes into account the nutritional balance that is in accordance with the season and weather. In this way, by proposing a menu that is in accordance with the season and weather, user satisfaction is improved.

[0131] The generation unit can optimize the combination of ingredients and cooking methods when generating a menu. For example, the generation unit can optimize the combination of ingredients input by the user to generate a balanced menu. The generation unit can also suggest the optimal cooking method for the ingredients input by the user to generate an efficient menu. The generation unit can also comprehensively optimize the combination of ingredients and cooking methods input by the user to generate a healthy menu. In this way, an efficient menu can be generated by optimizing the combination of ingredients and cooking methods.

[0132] The generation unit can refer to the user's past menu history to generate a menu that does not overlap. For example, the generation unit can refer to menus the user has made in the past and generate a new menu that does not include duplicate dishes. The generation unit can also analyze the user's past menu history to suggest menus with a wide variety. The generation unit can also take into account the frequency with which the user has made dishes in the past and preferentially suggest dishes that are not made frequently. In this way, by referring to the past menu history, a wide variety of menus can be suggested.

[0133] The generation unit can give priority to the use of specific ingredients and cooking utensils when generating a menu. For example, the generation unit generates a menu that gives priority to the use of specific ingredients input by the user. The generation unit can also generate a menu that uses specific cooking utensils (e.g., pressure cooker, frying pan) that the user owns. The generation unit can also generate an optimal menu by taking into account specific ingredients and cooking utensils that the user prefers. In this way, a menu that suits the user's preferences can be generated by giving priority to the use of specific ingredients and cooking utensils.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The reception unit receives user information. User information includes, for example, ingredients, cooking time, and family preferences. Users can input the ingredients they have in their refrigerator, the time they can spend cooking, and their family's allergies and preferences. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit evaluates the balance of calories, vitamins, and minerals to generate a menu that takes nutritional balance into consideration. Step 3: The generation unit generates a menu based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a menu that includes specific recipes and cooking procedures, and suggests dishes such as "stir-fried chicken and vegetables" or "potato soup." Step 4: The providing unit provides the menu generated by the generating unit. The providing unit provides the generated menu to the user and generates a list of necessary ingredients. For example, a list such as "chicken, carrots, potatoes, onions, and soup base" is generated.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 7, a 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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 AI 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.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 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 user 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. A system characterized by:

2. The reception unit Accepts user information about ingredients, cooking time, and family preferences 2. The system of claim 1.

3. The analysis unit The information received by the receiving unit is analyzed, and a menu based on nutritional balance is generated.

2. The system of claim 1.

4. The generation unit Generative AI generates menus including specific recipes and cooking steps 2. The system of claim 1.

5. The providing unit Present the generated menu to the user and generate a list of required ingredients 2. The system of claim 1.

6. It also has a memory unit that stores the user's past input information and uses it for the next suggestion.

2. The system of claim 1.

7. The device further includes a display unit that displays the nutritional balance of the generated menu.

2. The system of claim 1.

8. The apparatus further includes a feedback unit that receives user feedback and provides the feedback to the generation unit.

2. The system of claim 1.

9. Further provided is an ordering section that enables online ordering of ingredients based on the proposed menu.

2. The system of claim 1.

Images