System

Abstract

To provide a system for efficiently creating a menu on the basis of unique information for each facility.SOLUTION: A system includes an input unit, a creation unit, and a check unit. The input unit inputs unique information for each facility. The creation part creates a menu in consideration of a plurality of conditions on the basis of the information input by the input part. The check unit checks the menu created by the creation unit.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 takes a great deal of time and effort for registered dietitians to create menus, taking into account a huge number of factors.

[0005] The system according to the embodiment aims to efficiently create menus based on information specific to each facility. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a creation unit, and a check unit. The input unit inputs information specific to each facility. The creation unit creates a menu based on the information input by the input unit, taking into consideration multiple conditions. The check unit checks the menu created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create menus based on information specific to each facility. [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 system according to an embodiment of the present invention uses a generation AI to improve the work efficiency and reduce the burden on registered dietitians. This system inputs information specific to each facility into the generation AI, which then automatically creates menus taking into account numerous conditions, and the registered dietitian then checks the created menus. For example, it is possible to easily create menus that take into account specific allergies and nutritional balance. This reduces a task that would have taken more than 20 hours using conventional methods to less than two hours using this system. This allows the use of generation AI to easily add various conditions that could not be accommodated by conventional nutrition management systems, enabling more flexible menu creation. For example, it is possible to easily create menus that take into account specific allergies and nutritional balance.

[0029] The dietitian support system according to the embodiment includes an input unit, a creation unit, and a check unit. The input unit inputs information specific to each facility. For example, information such as the size of the facility, the user's age group, and specific dietary restrictions can be input. The input unit can also automatically collect information specific to each facility using a generation AI. The creation unit creates a menu based on the information input by the input unit, taking into account multiple conditions. For example, the creation unit can create a menu that takes into account specific allergies and nutritional balance. The creation unit uses the generation AI to analyze the input information and automatically create an optimal menu. The check unit checks the menu created by the creation unit and makes corrections as necessary. For example, the check unit checks whether the generated menu is error-free and whether the nutritional balance is appropriate. The check unit can also automatically check the generated menu using the generation AI. As a result, the dietitian support system according to the embodiment improves the work efficiency and reduces the burden on registered dietitians. For example, a task that would take more than 20 hours using conventional methods can be reduced to less than 2 hours using this system.

[0030] The input unit can input information specific to each facility. The input unit can input information such as the size of the facility, the age group of users, and specific dietary restrictions. The input unit can also automatically collect information specific to each facility using a generation AI. By inputting information specific to each facility, it becomes possible to create menus that meet individual needs. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input information such as the size of the facility and the age group of users into the generation AI, and the generation AI can automatically collect the information.

[0031] The creation unit can analyze the input information and generate an appropriate menu. The creation unit can, for example, analyze the input information and generate an optimal menu. For example, the creation unit can generate a menu for patients with specific allergies or a menu that takes into account a specific nutritional balance. The creation unit can also analyze the input information and automatically generate an optimal menu using a generation AI. In this way, the optimal menu can be automatically generated by analyzing the input information. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the input information into the generation AI, and the generation AI can automatically generate a menu.

[0032] The checking unit can check the generated menu and make corrections as necessary. For example, the checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. For example, the checking unit can check the nutritional value of the generated menu and check whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. In this way, by checking the generated menu and making corrections as necessary, the quality of the final menu can be ensured. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically check it.

[0033] The creation unit can generate a menu that takes into account specific allergies and nutritional balance. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking into account nutritional balance. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0034] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0035] The input unit can analyze the facility's past data and select an appropriate input method. The input unit, for example, analyzes the facility's past data and selects the most efficient input method. For example, the input unit analyzes past menu data and user feedback to suggest the optimal input method. The input unit can also customize the input method based on the facility's past data to achieve efficient information collection. This makes it possible to select the optimal input method and collect information efficiently by analyzing the facility's past data. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input the facility's past data into the generation AI, which then automatically selects the input method.

[0036] The input unit can provide guidelines for inputting the specific needs and conditions of the facility in detail at the time of input. The input unit provides, for example, guidelines for inputting the specific needs and conditions of the facility in detail. For example, the input unit provides guidelines for inputting the required information in detail at the time of input based on the specific conditions of the facility. The input unit can also provide guidelines for inputting the required information in detail at the time of input according to the specific needs of the facility. This enables efficient information collection by providing guidelines for inputting the specific needs and conditions of the facility in detail. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the specific needs and conditions of the facility into the generation AI, and the generation AI can automatically provide guidelines.

[0037] The input unit can efficiently collect information using voice input or image recognition during input. The input unit, for example, uses voice input to efficiently collect specific needs and conditions of the facility. For example, the input unit can automatically convert the specific needs and conditions of the facility into text data using voice recognition software. The input unit can also efficiently collect specific needs and conditions of the facility using image recognition. For example, the input unit can use a camera to capture specific information about the facility and analyze the information using an image recognition algorithm. The input unit can also combine voice input and image recognition to efficiently collect specific needs and conditions of the facility. This enables efficient information collection using voice input or image recognition. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input voice data or image data into the generation AI, which can then automatically analyze the information.

[0038] During input, the input unit can prioritize inputting highly relevant information by taking into account the geographical location information of the facility. The input unit prioritizes inputting highly relevant information by taking into account, for example, the geographical location information of the facility. For example, the input unit selects the optimal input method based on the address of the facility and the characteristics of the area. The input unit can also achieve efficient information collection by referring to the geographical location information of the facility. This makes it possible to efficiently collect highly relevant information by taking into account the geographical location information of the facility. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the geographical location information of the facility to the generation AI, and the generation AI can automatically select highly relevant information.

[0039] The input unit can analyze the facility's social media activity and input relevant information at the time of input. The input unit, for example, analyzes the facility's social media activity and inputs relevant information. For example, the input unit analyzes the content of the facility's posts and the reactions of followers, and inputs information optimal for specific conditions. The input unit can also customize the input method based on the facility's social media activity to achieve efficient information collection. This makes it possible to efficiently collect relevant information by analyzing the facility's social media activity. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the facility's social media data into the generation AI, and the generation AI can automatically analyze the information.

[0040] The input unit can customize the input method by reflecting the facility's past feedback at the time of input. The input unit customizes the input method by reflecting, for example, the facility's past feedback. For example, the input unit suggests the optimal input method for specific conditions based on past user evaluations and areas for improvement. The input unit can also customize the input method based on the facility's past feedback to achieve efficient information collection. This enables efficient information collection by reflecting the facility's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the facility's past feedback data into the generation AI, which then automatically customizes the input method.

[0041] The creation unit can generate a menu that takes into account specific allergies and nutritional balance during creation. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking nutritional balance into consideration. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0042] The creation unit can apply an algorithm to reflect the specific needs and conditions of the facility in detail when creating the menu. The creation unit, for example, applies an algorithm to generate an optimal menu based on the specific needs of the facility. For example, the creation unit applies an algorithm to generate a customized menu that reflects the specific conditions of the facility in detail. The creation unit can also apply an algorithm to realize efficient menu creation by taking the specific needs and conditions of the facility into consideration. In this way, efficient menu creation is possible by applying an algorithm to reflect the specific needs and conditions of the facility in detail. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the specific needs and conditions of the facility into the generation AI, and the generation AI can automatically apply the algorithm.

[0043] The creation unit can refer to past menu data when creating a menu to generate an appropriate menu. The creation unit, for example, references past menu data to generate an optimal menu. For example, the creation unit suggests an optimal menu for specific conditions from past menu data. The creation unit can also realize efficient menu creation based on past menu data. This makes it possible to generate an optimal menu by referring to past menu data. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input past menu data into the generation AI, which then automatically generates an optimal menu.

[0044] The creation unit can determine the priority of menus based on the time of menu submission. For example, the creation unit creates the most important menu with priority based on the time of menu submission. The creation unit can also realize efficient menu creation by taking into account the time of menu submission. The creation unit can also determine the priority of optimal menus based on the time of menu submission. In this way, efficient menu creation is possible by determining the priority based on the time of menu submission. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the time of menu submission into the generation AI, and the generation AI can automatically determine the priority.

[0045] The creation unit can adjust the order of the menu items based on the relevance of the menu items. For example, the creation unit provides menu items in an optimal order based on the relevance of the menu items. The creation unit can also realize efficient menu creation by taking the relevance of the menu items into consideration. The creation unit can also determine the optimal order of the menu items based on the relevance of the menu items. This enables efficient menu creation by adjusting the order based on the relevance of the menu items. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the relevance of the menu items into the generation AI, and the generation AI can automatically adjust the order.

[0046] The creation unit can adjust the use of technical terms in the menu according to the facility's level of expertise. For example, the creation unit determines whether to use technical terms according to the facility's level of expertise. The creation unit can also use appropriate technical terms taking into account the facility's level of expertise. The creation unit can also adjust the use of optimal technical terms based on the facility's level of expertise. This makes it possible to provide more appropriate menus by adjusting the use of technical terms according to the facility's level of expertise. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the facility's level of expertise into the generation AI, and the generation AI can automatically adjust the use of technical terms.

[0047] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0048] The check unit can provide guidelines for reflecting in detail the specific needs and conditions of the facility during the check. The check unit provides guidelines for reflecting in detail the specific needs and conditions of the facility. For example, the check unit provides guidelines for confirming in detail the information required during the check based on the specific conditions of the facility. The check unit can also provide guidelines for confirming in detail the information required during the check according to the specific needs of the facility. This enables efficient checks by providing guidelines for reflecting in detail the specific needs and conditions of the facility. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the specific needs and conditions of the facility into the generation AI, which then automatically provides guidelines.

[0049] When performing a check, the check unit can select the optimal check method by referring to past check data. The check unit, for example, selects the optimal check method by referring to past check data. For example, the check unit proposes the optimal check method for specific conditions from the past check data. The check unit can also realize an efficient check method based on the past check data. This makes it possible to select the optimal check method by referring to past check data and perform efficient checks. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input past check data into the generation AI, which then automatically selects the optimal check method.

[0050] When checking, the checking unit can prioritize checking highly relevant items by taking into account the geographical location information of the facility. For example, the checking unit prioritizes checking highly relevant check items by taking into account the geographical location information of the facility. The checking unit can also select an optimal checking method based on the geographical location information of the facility. The checking unit can also implement efficient checking by referring to the geographical location information of the facility. In this way, highly relevant items can be efficiently checked by taking the geographical location information of the facility into account. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the geographical location information of the facility into the generation AI, and the generation AI can automatically select highly relevant items.

[0051] During a check, the checking unit can analyze the facility's social media activity and check related items. For example, the checking unit can analyze the facility's social media activity and confirm related check items. The checking unit can also suggest check items that are optimal for specific conditions based on the facility's social media activity. The checking unit can also implement efficient checks based on the facility's social media activity. This allows related items to be checked efficiently by analyzing the facility's social media activity. Some or all of the above-mentioned processing in the checking unit may be performed using or without the generation AI. For example, the checking unit can input the facility's social media data into the generation AI, which can then automatically analyze the information.

[0052] The check unit can customize the check method by reflecting the facility's past feedback when checking. The check unit, for example, customizes the check method by reflecting the facility's past feedback. For example, the check unit suggests the optimal check method for specific conditions based on past user evaluations and areas for improvement. The check unit can also customize the check method based on the facility's past feedback to achieve efficient checks. This enables efficient checks by reflecting the facility's past feedback. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the facility's past feedback data into the generation AI, which can then automatically customize the check method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The input unit can analyze the facility's past data and select an appropriate input method. The input unit, for example, analyzes the facility's past data and selects the most efficient input method. For example, the input unit analyzes past menu data and user feedback to suggest the optimal input method. The input unit can also customize the input method based on the facility's past data to achieve efficient information collection. This makes it possible to select the optimal input method and collect information efficiently by analyzing the facility's past data. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input the facility's past data into the generation AI, which then automatically selects the input method.

[0055] The creation unit can generate a menu that takes into account specific allergies and nutritional balance during creation. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking nutritional balance into consideration. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0056] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0057] The input unit can efficiently collect information using voice input or image recognition during input. The input unit, for example, uses voice input to efficiently collect specific needs and conditions of the facility. For example, the input unit can automatically convert the specific needs and conditions of the facility into text data using voice recognition software. The input unit can also efficiently collect specific needs and conditions of the facility using image recognition. For example, the input unit can use a camera to capture specific information about the facility and analyze the information using an image recognition algorithm. The input unit can also combine voice input and image recognition to efficiently collect specific needs and conditions of the facility. This enables efficient information collection using voice input or image recognition. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input voice data or image data into the generation AI, which can then automatically analyze the information.

[0058] The creation unit can determine the priority of menus based on the time of menu submission. For example, the creation unit creates the most important menu with priority based on the time of menu submission. The creation unit can also realize efficient menu creation by taking into account the time of menu submission. The creation unit can also determine the priority of optimal menus based on the time of menu submission. In this way, efficient menu creation is possible by determining the priority based on the time of menu submission. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the time of menu submission into the generation AI, and the generation AI can automatically determine the priority.

[0059] The check unit can provide guidelines for reflecting in detail the specific needs and conditions of the facility during the check. The check unit provides guidelines for reflecting in detail the specific needs and conditions of the facility. For example, the check unit provides guidelines for confirming in detail the information required during the check based on the specific conditions of the facility. The check unit can also provide guidelines for confirming in detail the information required during the check according to the specific needs of the facility. This enables efficient checks by providing guidelines for reflecting in detail the specific needs and conditions of the facility. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the specific needs and conditions of the facility into the generation AI, which then automatically provides guidelines.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input unit inputs information specific to each facility. For example, information such as the size of the facility, the age group of users, and specific dietary restrictions can be input. The input unit can also use generative AI to automatically collect information specific to each facility. Step 2: The creation unit creates a menu based on the information input by the input unit, taking into account multiple conditions. For example, the creation unit can generate a menu that takes into account specific allergies and nutritional balance. The creation unit uses generation AI to analyze the input information and automatically generate the optimal menu. Step 3: The checking unit checks the menu created by the creation unit and makes corrections as necessary. For example, the checking unit checks whether the created menu has any errors or whether the nutritional balance is appropriate. The checking unit can also use the generation AI to automatically check the created menu.

[0062] (Example 2) A system according to an embodiment of the present invention uses a generation AI to improve the work efficiency and reduce the burden on registered dietitians. This system inputs information specific to each facility into the generation AI, which then automatically creates menus taking into account numerous conditions, and the registered dietitian then checks the created menus. For example, it is possible to easily create menus that take into account specific allergies and nutritional balance. This reduces a task that would have taken more than 20 hours using conventional methods to less than two hours using this system. This allows the use of generation AI to easily add various conditions that could not be accommodated by conventional nutrition management systems, enabling more flexible menu creation. For example, it is possible to easily create menus that take into account specific allergies and nutritional balance.

[0063] The dietitian support system according to the embodiment includes an input unit, a creation unit, and a check unit. The input unit inputs information specific to each facility. For example, information such as the size of the facility, the user's age group, and specific dietary restrictions can be input. The input unit can also automatically collect information specific to each facility using a generation AI. The creation unit creates a menu based on the information input by the input unit, taking into account multiple conditions. For example, the creation unit can create a menu that takes into account specific allergies and nutritional balance. The creation unit uses the generation AI to analyze the input information and automatically create an optimal menu. The check unit checks the menu created by the creation unit and makes corrections as necessary. For example, the check unit checks whether the generated menu is error-free and whether the nutritional balance is appropriate. The check unit can also automatically check the generated menu using the generation AI. As a result, the dietitian support system according to the embodiment improves the work efficiency and reduces the burden on registered dietitians. For example, a task that would take more than 20 hours using conventional methods can be reduced to less than 2 hours using this system.

[0064] The input unit can input information specific to each facility. The input unit can input information such as the size of the facility, the age group of users, and specific dietary restrictions. The input unit can also automatically collect information specific to each facility using a generation AI. By inputting information specific to each facility, it becomes possible to create menus that meet individual needs. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input information such as the size of the facility and the age group of users into the generation AI, and the generation AI can automatically collect the information.

[0065] The creation unit can analyze the input information and generate an appropriate menu. The creation unit can, for example, analyze the input information and generate an optimal menu. For example, the creation unit can generate a menu for patients with specific allergies or a menu that takes into account a specific nutritional balance. The creation unit can also analyze the input information and automatically generate an optimal menu using a generation AI. In this way, the optimal menu can be automatically generated by analyzing the input information. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the input information into the generation AI, and the generation AI can automatically generate a menu.

[0066] The checking unit can check the generated menu and make corrections as necessary. For example, the checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. For example, the checking unit can check the nutritional value of the generated menu and check whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. In this way, by checking the generated menu and making corrections as necessary, the quality of the final menu can be ensured. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically check it.

[0067] The creation unit can generate a menu that takes into account specific allergies and nutritional balance. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking into account nutritional balance. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0068] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0069] The dietitian support system includes an input unit that estimates a user's emotions and adjusts input timing based on the estimated user emotions. For example, if the user is feeling stressed, the input unit delays the input timing to provide a relaxing environment. Furthermore, if the user is relaxed, the input unit can also accelerate the input timing to efficiently collect information. Furthermore, if the user is in a hurry, the input unit can also optimize the input timing to allow the user to quickly enter information. This enables efficient information collection by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using the generation AI, or may be performed without the generation AI. For example, the input unit can input the user's emotion data into the generation AI, which can then automatically adjust the input timing.

[0070] The input unit can analyze the facility's past data and select an appropriate input method. The input unit, for example, analyzes the facility's past data and selects the most efficient input method. For example, the input unit analyzes past menu data and user feedback to suggest the optimal input method. The input unit can also customize the input method based on the facility's past data to achieve efficient information collection. This makes it possible to select the optimal input method and collect information efficiently by analyzing the facility's past data. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input the facility's past data into the generation AI, which then automatically selects the input method.

[0071] The input unit can provide guidelines for inputting the specific needs and conditions of the facility in detail at the time of input. The input unit provides, for example, guidelines for inputting the specific needs and conditions of the facility in detail. For example, the input unit provides guidelines for inputting the required information in detail at the time of input based on the specific conditions of the facility. The input unit can also provide guidelines for inputting the required information in detail at the time of input according to the specific needs of the facility. This enables efficient information collection by providing guidelines for inputting the specific needs and conditions of the facility in detail. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the specific needs and conditions of the facility into the generation AI, and the generation AI can automatically provide guidelines.

[0072] The input unit can efficiently collect information using voice input or image recognition during input. The input unit, for example, uses voice input to efficiently collect specific needs and conditions of the facility. For example, the input unit can automatically convert the specific needs and conditions of the facility into text data using voice recognition software. The input unit can also efficiently collect specific needs and conditions of the facility using image recognition. For example, the input unit can use a camera to capture specific information about the facility and analyze the information using an image recognition algorithm. The input unit can also combine voice input and image recognition to efficiently collect specific needs and conditions of the facility. This enables efficient information collection using voice input or image recognition. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input voice data or image data into the generation AI, which can then automatically analyze the information.

[0073] The input unit can estimate the user's emotions and prioritize the information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the input unit can prioritize inputting important information. Furthermore, when the user is relaxed, the input unit can prioritize inputting detailed information. Furthermore, when the user is in a hurry, the input unit can prioritize information to be quickly input. This enables efficient information collection by prioritizing the information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the input unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI, which can then automatically prioritize the information.

[0074] During input, the input unit can prioritize inputting highly relevant information by taking into account the geographical location information of the facility. The input unit prioritizes inputting highly relevant information by taking into account, for example, the geographical location information of the facility. For example, the input unit selects the optimal input method based on the address of the facility and the characteristics of the area. The input unit can also achieve efficient information collection by referring to the geographical location information of the facility. This makes it possible to efficiently collect highly relevant information by taking into account the geographical location information of the facility. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the geographical location information of the facility to the generation AI, and the generation AI can automatically select highly relevant information.

[0075] The input unit can analyze the facility's social media activity and input relevant information at the time of input. The input unit, for example, analyzes the facility's social media activity and inputs relevant information. For example, the input unit analyzes the content of the facility's posts and the reactions of followers, and inputs information optimal for specific conditions. The input unit can also customize the input method based on the facility's social media activity to achieve efficient information collection. This makes it possible to efficiently collect relevant information by analyzing the facility's social media activity. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the facility's social media data into the generation AI, and the generation AI can automatically analyze the information.

[0076] The input unit can customize the input method by reflecting the facility's past feedback at the time of input. The input unit customizes the input method by reflecting, for example, the facility's past feedback. For example, the input unit suggests the optimal input method for specific conditions based on past user evaluations and areas for improvement. The input unit can also customize the input method based on the facility's past feedback to achieve efficient information collection. This enables efficient information collection by reflecting the facility's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the facility's past feedback data into the generation AI, which then automatically customizes the input method.

[0077] The creation unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user emotions. For example, when the user is relaxed, the creation unit uses a visually relaxing presentation method. Furthermore, when the user is in a hurry, the creation unit can use a concise and to-the-point presentation method. Furthermore, when the user is excited, the creation unit can use a visually stimulating presentation method. This allows the menu presentation method to be adjusted according to the user's emotions, thereby providing a more appropriate menu. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which then automatically adjusts the menu presentation method.

[0078] The creation unit can generate a menu that takes into account specific allergies and nutritional balance during creation. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking nutritional balance into consideration. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0079] The creation unit can apply an algorithm to reflect the specific needs and conditions of the facility in detail when creating the menu. The creation unit, for example, applies an algorithm to generate an optimal menu based on the specific needs of the facility. For example, the creation unit applies an algorithm to generate a customized menu that reflects the specific conditions of the facility in detail. The creation unit can also apply an algorithm to realize efficient menu creation by taking the specific needs and conditions of the facility into consideration. In this way, efficient menu creation is possible by applying an algorithm to reflect the specific needs and conditions of the facility in detail. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the specific needs and conditions of the facility into the generation AI, and the generation AI can automatically apply the algorithm.

[0080] The creation unit can refer to past menu data when creating a menu to generate an appropriate menu. The creation unit, for example, references past menu data to generate an optimal menu. For example, the creation unit suggests an optimal menu for specific conditions from past menu data. The creation unit can also realize efficient menu creation based on past menu data. This makes it possible to generate an optimal menu by referring to past menu data. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input past menu data into the generation AI, which then automatically generates an optimal menu.

[0081] The creation unit can estimate the user's emotions and adjust the length of the menu based on the estimated user emotions. For example, the creation unit can provide a detailed menu when the user is relaxed. The creation unit can also provide a concise menu when the user is in a hurry. The creation unit can also provide a visually stimulating menu when the user is excited. This allows the length of the menu to be adjusted according to the user's emotions, thereby providing a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which can then automatically adjust the length of the menu.

[0082] The creation unit can determine the priority of menus based on the time of menu submission. For example, the creation unit creates the most important menu with priority based on the time of menu submission. The creation unit can also realize efficient menu creation by taking into account the time of menu submission. The creation unit can also determine the priority of optimal menus based on the time of menu submission. In this way, efficient menu creation is possible by determining the priority based on the time of menu submission. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the time of menu submission into the generation AI, and the generation AI can automatically determine the priority.

[0083] The creation unit can adjust the order of the menu items based on the relevance of the menu items. For example, the creation unit provides menu items in an optimal order based on the relevance of the menu items. The creation unit can also realize efficient menu creation by taking the relevance of the menu items into consideration. The creation unit can also determine the optimal order of the menu items based on the relevance of the menu items. This enables efficient menu creation by adjusting the order based on the relevance of the menu items. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the relevance of the menu items into the generation AI, and the generation AI can automatically adjust the order.

[0084] The creation unit can adjust the use of technical terms in the menu according to the facility's level of expertise. For example, the creation unit determines whether to use technical terms according to the facility's level of expertise. The creation unit can also use appropriate technical terms taking into account the facility's level of expertise. The creation unit can also adjust the use of optimal technical terms based on the facility's level of expertise. This makes it possible to provide more appropriate menus by adjusting the use of technical terms according to the facility's level of expertise. Some or all of the above-mentioned processing in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the facility's level of expertise into the generation AI, and the generation AI can automatically adjust the use of technical terms.

[0085] The check unit can estimate the user's emotions and adjust the check method based on the estimated user emotions. For example, the check unit can perform a detailed check when the user is relaxed. The check unit can also perform a brief check when the user is in a hurry. The check unit can also perform a visually stimulating check when the user is excited. This enables efficient checks by adjusting the check method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the check unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the check unit can input the user's emotion data into the generation AI, which can then automatically adjust the check method.

[0086] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0087] The check unit can provide guidelines for reflecting in detail the specific needs and conditions of the facility during the check. The check unit provides guidelines for reflecting in detail the specific needs and conditions of the facility. For example, the check unit provides guidelines for confirming in detail the information required during the check based on the specific conditions of the facility. The check unit can also provide guidelines for confirming in detail the information required during the check according to the specific needs of the facility. This enables efficient checks by providing guidelines for reflecting in detail the specific needs and conditions of the facility. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the specific needs and conditions of the facility into the generation AI, which then automatically provides guidelines.

[0088] When performing a check, the check unit can select the optimal check method by referring to past check data. The check unit, for example, selects the optimal check method by referring to past check data. For example, the check unit proposes the optimal check method for specific conditions from the past check data. The check unit can also realize an efficient check method based on the past check data. This makes it possible to select the optimal check method by referring to past check data and perform efficient checks. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input past check data into the generation AI, which then automatically selects the optimal check method.

[0089] The check unit can estimate the user's emotions and determine the priority of check items based on the estimated user emotions. For example, if the user is relaxed, the check unit can prioritize detailed check items. Furthermore, if the user is in a hurry, the check unit can prioritize important check items. Furthermore, if the user is excited, the check unit can prioritize visually stimulating check items. This enables efficient checking by determining the priority of check items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the check unit can be performed using the generation AI, or can be performed without the generation AI. For example, the check unit can input the user's emotion data into the generation AI, which can then automatically determine the priority of check items.

[0090] When checking, the checking unit can prioritize checking highly relevant items by taking into account the geographical location information of the facility. For example, the checking unit prioritizes checking highly relevant check items by taking into account the geographical location information of the facility. The checking unit can also select an optimal checking method based on the geographical location information of the facility. The checking unit can also implement efficient checking by referring to the geographical location information of the facility. In this way, highly relevant items can be efficiently checked by taking the geographical location information of the facility into account. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the geographical location information of the facility into the generation AI, and the generation AI can automatically select highly relevant items.

[0091] During a check, the checking unit can analyze the facility's social media activity and check related items. For example, the checking unit can analyze the facility's social media activity and confirm related check items. The checking unit can also suggest check items that are optimal for specific conditions based on the facility's social media activity. The checking unit can also implement efficient checks based on the facility's social media activity. This allows related items to be checked efficiently by analyzing the facility's social media activity. Some or all of the above-mentioned processing in the checking unit may be performed using or without the generation AI. For example, the checking unit can input the facility's social media data into the generation AI, which can then automatically analyze the information.

[0092] The check unit can customize the check method by reflecting the facility's past feedback when checking. The check unit, for example, customizes the check method by reflecting the facility's past feedback. For example, the check unit suggests the optimal check method for specific conditions based on past user evaluations and areas for improvement. The check unit can also customize the check method based on the facility's past feedback to achieve efficient checks. This enables efficient checks by reflecting the facility's past feedback. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the facility's past feedback data into the generation AI, which can then automatically customize the check method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, creation unit, and check unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input facility-specific information using the reception device 38 of the smart device 14, and the information can be automatically collected by the specific processing unit 290 of the data processing device 12. The creation unit analyzes the information input by the specific processing unit 290 of the data processing device 12 and generates an optimal menu. The check unit checks the menu generated by the control unit 46A of the smart device 14 and makes corrections as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, creation unit, and check 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 input unit can input facility-specific information using the microphone 238 of the smart glasses 214, which can then be automatically collected by the specific processing unit 290 of the data processing device 12. The creation unit analyzes the information input by the specific processing unit 290 of the data processing device 12 and generates an optimal menu. The check unit checks the menu generated by the control unit 46A of the smart glasses 214 and makes corrections as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, creation unit, and check unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can input facility-specific information using the microphone 238 of the headset terminal 314, which can then be automatically collected by the specific processing unit 290 of the data processing device 12. The creation unit analyzes the information input by the specific processing unit 290 of the data processing device 12 and generates an optimal menu. The check unit checks the menu generated by the control unit 46A of the headset terminal 314 and makes corrections as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, creation unit, and check unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input facility-specific information using the microphone 238 of the robot 414, which can then be automatically collected by the specific processing unit 290 of the data processing device 12. The creation unit analyzes the information input by the specific processing unit 290 of the data processing device 12 and generates an optimal menu. The check unit checks the menu generated by the control unit 46A of the robot 414 and makes corrections as necessary.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The dietitian support system includes an input unit that estimates a user's emotions and adjusts input timing based on the estimated user emotions. For example, if the user is feeling stressed, the input unit delays the input timing to provide a relaxing environment. Furthermore, if the user is relaxed, the input unit can also accelerate the input timing to efficiently collect information. Furthermore, if the user is in a hurry, the input unit can also optimize the input timing to allow the user to quickly enter information. This enables efficient information collection by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using the generation AI, or may be performed without the generation AI. For example, the input unit can input the user's emotion data into the generation AI, which can then automatically adjust the input timing.

[0095] The input unit can analyze the facility's past data and select an appropriate input method. The input unit, for example, analyzes the facility's past data and selects the most efficient input method. For example, the input unit analyzes past menu data and user feedback to suggest the optimal input method. The input unit can also customize the input method based on the facility's past data to achieve efficient information collection. This makes it possible to select the optimal input method and collect information efficiently by analyzing the facility's past data. Some or all of the above-mentioned processing in the input unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the input unit can input the facility's past data into the generation AI, which then automatically selects the input method.

[0096] The creation unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user emotions. For example, when the user is relaxed, the creation unit uses a visually relaxing presentation method. Furthermore, when the user is in a hurry, the creation unit can use a concise and to-the-point presentation method. Furthermore, when the user is excited, the creation unit can use a visually stimulating presentation method. This allows the menu presentation method to be adjusted according to the user's emotions, thereby providing a more appropriate menu. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which then automatically adjusts the menu presentation method.

[0097] The creation unit can generate a menu that takes into account specific allergies and nutritional balance during creation. For example, the creation unit can generate a menu that does not contain allergenic ingredients for a patient with a specific allergy. For example, the creation unit can generate a nut-free menu for a patient with a nut allergy. The creation unit can also generate a menu that is fortified with specific nutrients while taking nutritional balance into consideration. For example, the creation unit can generate a menu that is fortified with vitamins and minerals. The creation unit can also generate an individually customized menu while taking into account specific allergies and nutritional balance. This makes it possible to create menus that meet individual needs by generating menus that take into account specific allergies and nutritional balance. Some or all of the above-mentioned processes in the creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input conditions that take into account specific allergies and nutritional balance into the generation AI, and the generation AI can automatically generate a menu.

[0098] The check unit can estimate the user's emotions and adjust the check method based on the estimated user emotions. For example, the check unit can perform a detailed check when the user is relaxed. The check unit can also perform a brief check when the user is in a hurry. The check unit can also perform a visually stimulating check when the user is excited. This enables efficient checks by adjusting the check method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the check unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the check unit can input the user's emotion data into the generation AI, which can then automatically adjust the check method.

[0099] The checking unit can check whether the generated menu has any errors and whether the nutritional balance is appropriate. The checking unit, for example, checks whether the generated menu has any errors. For example, the checking unit checks the nutritional value of the generated menu and checks whether it contains any allergenic ingredients. The checking unit can also evaluate the taste of the generated menu and make corrections as necessary. This makes it possible to ensure the quality of the final menu by checking whether the generated menu has any errors and whether the nutritional balance is appropriate. Some or all of the above-mentioned processing in the checking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the checking unit can input the generated menu into the generation AI, and the generation AI can automatically perform the check.

[0100] The input unit can efficiently collect information using voice input or image recognition during input. The input unit, for example, uses voice input to efficiently collect specific needs and conditions of the facility. For example, the input unit can automatically convert the specific needs and conditions of the facility into text data using voice recognition software. The input unit can also efficiently collect specific needs and conditions of the facility using image recognition. For example, the input unit can use a camera to capture specific information about the facility and analyze the information using an image recognition algorithm. The input unit can also combine voice input and image recognition to efficiently collect specific needs and conditions of the facility. This enables efficient information collection using voice input or image recognition. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input voice data or image data into the generation AI, which can then automatically analyze the information.

[0101] The creation unit can estimate the user's emotions and adjust the length of the menu based on the estimated user emotions. For example, the creation unit can provide a detailed menu when the user is relaxed. The creation unit can also provide a concise menu when the user is in a hurry. The creation unit can also provide a visually stimulating menu when the user is excited. This allows the length of the menu to be adjusted according to the user's emotions, thereby providing a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which can then automatically adjust the length of the menu.

[0102] The creation unit can determine the priority of menus based on the time of menu submission. For example, the creation unit creates the most important menu with priority based on the time of menu submission. The creation unit can also realize efficient menu creation by taking into account the time of menu submission. The creation unit can also determine the priority of optimal menus based on the time of menu submission. In this way, efficient menu creation is possible by determining the priority based on the time of menu submission. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the time of menu submission into the generation AI, and the generation AI can automatically determine the priority.

[0103] The check unit can provide guidelines for reflecting in detail the specific needs and conditions of the facility during the check. The check unit provides guidelines for reflecting in detail the specific needs and conditions of the facility. For example, the check unit provides guidelines for confirming in detail the information required during the check based on the specific conditions of the facility. The check unit can also provide guidelines for confirming in detail the information required during the check according to the specific needs of the facility. This enables efficient checks by providing guidelines for reflecting in detail the specific needs and conditions of the facility. Some or all of the above-mentioned processing in the check unit may be performed using or without the generation AI. For example, the check unit can input the specific needs and conditions of the facility into the generation AI, which then automatically provides guidelines.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The input unit inputs information specific to each facility. For example, information such as the size of the facility, the age group of users, and specific dietary restrictions can be input. The input unit can also use generative AI to automatically collect information specific to each facility. Step 2: The creation unit creates a menu based on the information input by the input unit, taking into account multiple conditions. For example, the creation unit can generate a menu that takes into account specific allergies and nutritional balance. The creation unit uses generation AI to analyze the input information and automatically generate the optimal menu. Step 3: The checking unit checks the menu created by the creation unit and makes corrections as necessary. For example, the checking unit checks whether the created menu has any errors or whether the nutritional balance is appropriate. The checking unit can also use the generation AI to automatically check the created menu.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the 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.

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

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0157] 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.

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

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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. An input section for inputting information specific to each facility; A creation unit that creates a menu based on the information input by the input unit, taking into consideration multiple conditions; A check unit that checks the menu created by the creation unit; Equipped with A system characterized by:

2. The input unit includes: Input specific information for each facility 2. The system of claim 1.

3. The creation unit Analyzes input information and generates appropriate menus 2. The system of claim 1.

4. The checking unit Check the generated menu and make any necessary adjustments.

2. The system of claim 1.

5. The creation unit Generate menus that take into account specific allergies and nutritional balance 2. The system of claim 1.

6. The checking unit Check whether the generated menu is correct and has the right nutritional balance 2. The system of claim 1.

7. The input unit includes: Estimate the user's emotions and adjust the timing of input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit includes: Analyze the facility's historical data and select the appropriate input method 2. The system of claim 1.

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