system

Abstract

The system according to the embodiment aims to enable a user to appropriately manage the nutritional balance of his or her own meals and plan the next meal menu. [Solution] A system according to an embodiment includes an input unit, an evaluation unit, a proposal unit, and a collaboration unit. The input unit accepts meal information from a user. The evaluation unit evaluates nutritional balance based on the meal information accepted by the input unit. The proposal unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. The collaboration unit collaborates with a delivery service based on the menu proposed by the proposal unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for users to properly manage the nutritional balance of their own meals and plan their next meal menu.

[0005] The system according to the embodiment aims to enable a user to appropriately manage the nutritional balance of his or her own meals and plan the next meal menu. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an evaluation unit, a proposal unit, and a collaboration unit. The input unit accepts meal information from a user. The evaluation unit evaluates nutritional balance based on the meal information accepted by the input unit. The proposal unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. The collaboration unit collaborates with a delivery service based on the menu proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to appropriately manage the nutritional balance of their own meals and plan their next meal menu. [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 nutritional balance suggestion system according to an embodiment of the present invention allows a user to input the meals they ate that day and proposes nutritionally balanced meal menus for the following day or the following day. This system also integrates with delivery services to deliver boxed lunches or pre-prepared meals for those who are not good at cooking. For example, a user inputs the meals they ate that day. This input includes detailed information such as the type, quantity, and ingredients of each meal. For example, input information such as "toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner." This information is then input into an AI. The AI ​​then analyzes the input meal information and evaluates the nutritional balance. The AI ​​retrieves the nutritional components of each ingredient from a database and compares them with the user's diet to calculate the nutritional balance. For example, it evaluates the excess or deficiency of nutrients such as vitamins, minerals, protein, carbohydrates, and lipids. The AI ​​then proposes a nutritionally balanced meal menu for the following day or the following day. For example, it suggests a menu such as "yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner." This suggestion also takes into account the user's preferences and allergies. Furthermore, for users who are not good at cooking at home, the system also has a function to deliver bento boxes or pre-prepared meals in cooperation with delivery services. For example, a bento box can be ordered from an affiliated delivery service based on a suggested menu and delivered to the user's home. At this time, the delivery service provides a nutritionally balanced meal based on the suggested menu. This system allows users to easily eat a nutritionally balanced meal. Furthermore, even people who are not good at cooking at home can easily enjoy healthy meals by using a delivery service. This system is extremely convenient for people who have difficulty cooking at home, such as busy businessmen and the elderly. In this way, the nutritional balance suggestion system allows users to easily eat a nutritionally balanced meal.

[0029] A nutritional balance suggestion system according to an embodiment includes an input unit, an evaluation unit, a suggestion unit, and a linking unit. The input unit accepts dietary information from a user. The dietary information from the user includes, but is not limited to, detailed information such as the type and amount of food and ingredients. The input unit, for example, inputs the contents of the meals the user ate in a day. For example, information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner can be input. The evaluation unit evaluates the nutritional balance based on the dietary information accepted by the input unit. For example, the evaluation unit obtains the nutritional components of each ingredient from a database and compares them with the user's dietary contents to calculate the nutritional balance. For example, the evaluation unit evaluates whether nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids are in excess or deficient. The suggestion unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. For example, the suggestion unit can propose a menu such as yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner. This suggestion also takes into account the user's preferences and allergy information. The collaboration unit collaborates with a delivery service based on the menu proposed by the proposal unit. For example, the collaboration unit can order a lunch box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. This allows the nutritional balance proposal system according to the embodiment to enable the user to easily eat a nutritionally balanced meal. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can select a delivery service using an AI model that receives the menu proposed by the proposal unit as input and outputs a selection of a delivery service.

[0030] The evaluation unit can obtain the nutritional components of each ingredient from the database and calculate the nutritional balance by comparing them with the user's dietary content. For example, the evaluation unit can obtain the nutritional components of each ingredient from the database and calculate the nutritional balance by comparing them with the user's dietary content. For example, the evaluation unit can evaluate excesses and deficiencies of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. The evaluation unit can also update the nutritional components of each ingredient in real time. For example, the evaluation unit can refer to the latest nutrition database and update the nutritional components of each ingredient. This enables accurate evaluation of nutritional balance by obtaining the nutritional components of each ingredient from the database. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI, or can be performed without AI. For example, the evaluation unit can evaluate nutritional balance using an AI model that inputs dietary information and outputs an evaluation of nutritional balance.

[0031] The suggestion unit can suggest a menu based on the user's preferences or allergy information. The suggestion unit, for example, suggests a menu taking into consideration the user's preferences and allergy information. For example, the suggestion unit suggests a menu based on the user's favorite ingredients and cooking methods. The suggestion unit can also suggest a menu that does not contain allergens based on the user's allergy information. For example, the suggestion unit suggests a menu that avoids ingredients to which the user is allergic. This makes it possible to suggest a more appropriate menu by taking into consideration the user's preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's preferences and allergy information and outputs menu suggestions.

[0032] The collaboration unit can order a bento box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. The collaboration unit can, for example, order a bento box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. For example, the collaboration unit can select a delivery service that provides nutritionally balanced bento boxes based on the proposed menu. The collaboration unit can also select an appropriate delivery service taking into account the user's preferences and allergy information. For example, the collaboration unit can select a delivery service that provides bento boxes that avoid ingredients to which the user is allergic. This allows the user to easily eat a nutritionally balanced meal by collaborating with a delivery service based on the proposed menu. Some or all of the above-described processing by the collaboration unit can be performed, for example, using AI or without AI. For example, the collaboration unit can select a delivery service using an AI model that inputs the proposed menu and outputs a selection of a delivery service.

[0033] The input unit can input information about the types or amounts of meals and ingredients eaten by the user in a day. The input unit inputs, for example, detailed information about the types, amounts, and ingredients of meals eaten by the user in a day. For example, the input unit can input information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner. The input unit can also provide an interface that allows the user to easily input information about the types, amounts, and ingredients of meals. For example, the input unit may provide drop-down menus or check boxes that allow the user to input meal information simply by selecting it. This allows the user to input detailed meal information, enabling a more accurate evaluation of nutritional balance. Some or all of the above-described processing in the input unit may be performed, for example, using AI or without AI. For example, the input unit can input the meal information entered by the user to the generation AI and cause the generation AI to analyze the meal information.

[0034] The input unit can analyze the user's past meal history and select the optimal input method. The input unit, for example, analyzes the user's past meal history and selects the optimal input method. For example, the input unit automatically displays meal information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit predicts and suggests meal information to be input during a specific time period based on the user's past meal history. In this way, by analyzing the past meal history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past meal history data to the generation AI and cause the generation AI to select the optimal input method.

[0035] The input unit can filter the dietary information based on the user's current health condition and goals when inputting the dietary information. For example, when inputting the dietary information, the input unit filters the dietary information based on the user's current health condition and goals. For example, if the user is on a diet, the input unit filters out high-calorie dietary information. Furthermore, if the user is aiming to build muscle, the input unit can preferentially display high-protein dietary information. For example, if the user has a specific health goal, the input unit suggests dietary information that matches the goal. This allows more appropriate dietary information to be input by filtering based on the user's health condition and goals. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's health condition and goal data to the generation AI and have the generation AI perform the filtering.

[0036] When inputting meal information, the input unit can prioritize inputting highly relevant meal information taking into account the user's geographical location information. For example, when inputting meal information, the input unit prioritizes inputting highly relevant meal information taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting ingredients and dishes from that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting meal information from the travel destination. For example, when the user is at home, the input unit can prioritize inputting ingredients that can be purchased at a nearby supermarket. In this way, more relevant meal information can be input by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant meal information.

[0037] The input unit can analyze the user's social media activity when inputting meal information and input related meal information. For example, the input unit can analyze the user's social media activity when inputting meal information and input related meal information. For example, the input unit can automatically input meal information shared by the user on social media. The input unit can also prioritize input of recipes from cooking accounts the user follows. For example, the input unit can input meal information that the user has "liked" on social media. This allows more relevant meal information to be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's social media data to the generation AI and cause the generation AI to select related meal information.

[0038] The evaluation unit can improve the accuracy of the evaluation when evaluating nutritional balance by referring to the user's past health data. For example, the evaluation unit can improve the accuracy of the evaluation when evaluating nutritional balance by referring to the user's past health data. For example, the evaluation unit can evaluate nutritional balance by referring to the user's past blood test results. The evaluation unit can also evaluate nutritional balance by referring to the user's past diet history. For example, the evaluation unit can evaluate nutritional balance by referring to the user's past exercise data. By referring to the past health data, the accuracy of the nutritional balance evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the user's past health data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0039] The evaluation unit can take into account the user's lifestyle habits and amount of exercise when evaluating nutritional balance. For example, the evaluation unit can take into account the user's lifestyle habits and amount of exercise when evaluating nutritional balance. For example, if the user exercises frequently, the evaluation unit can evaluate by focusing on protein intake. Furthermore, if the user lives a desk-based lifestyle, the evaluation unit can also evaluate by focusing on vitamin and mineral intake. For example, if the user works night shifts, the evaluation unit can evaluate by focusing on nutrients that are effective for energy replenishment. This enables a more appropriate evaluation of nutritional balance by taking the user's lifestyle habits and amount of exercise into consideration. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input data on the user's lifestyle habits and amount of exercise into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0040] The evaluation unit can take the user's geographical distribution into consideration when evaluating the nutritional balance. For example, the evaluation unit can evaluate the nutritional balance by taking the user's geographical distribution into consideration when evaluating the nutritional balance. For example, the evaluation unit can evaluate the nutritional balance by taking the food culture of the area where the user lives into consideration. Furthermore, if the user is traveling, the evaluation unit can also evaluate the nutritional balance by taking the food culture of the travel destination into consideration. For example, if the user is in a specific area, the evaluation unit can evaluate the nutritional balance by taking the ingredients of that area into consideration. This allows for a more appropriate evaluation of the nutritional balance by taking the user's geographical distribution into consideration. Some or all of the above-described processing by the evaluation unit can be performed using, or without, AI, for example. For example, the evaluation unit can input the user's geographical distribution data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0041] The evaluation unit can improve the accuracy of the evaluation by referring to the user's related literature when evaluating nutritional balance. For example, the evaluation unit improves the accuracy of the evaluation by referring to the user's related literature when evaluating nutritional balance. For example, the evaluation unit makes the evaluation by referring to nutrition-related literature read by the user. The evaluation unit can also make the evaluation by referring to research by nutritionists the user follows. For example, the evaluation unit makes the evaluation by referring to materials from nutrition seminars attended by the user. By doing so, the accuracy of the nutritional balance evaluation is improved by referring to the related literature. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit may input the user's related literature data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0042] The suggestion unit can suggest an optimal menu by referring to the user's past meal history when making a suggestion. For example, the suggestion unit suggests an optimal menu by referring to the user's past meal history when making a suggestion. For example, the suggestion unit makes a suggestion based on menus that the user has previously preferred. The suggestion unit can also suggest a nutritionally balanced menu from the user's past meal history. For example, the suggestion unit analyzes the user's past meal history and suggests the most suitable menu. In this way, the optimal menu can be suggested to the user by referring to the past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past meal history data into the generation AI and cause the generation AI to suggest an optimal menu.

[0043] The suggestion unit can customize the suggestion content based on the user's current health condition and goals when making a suggestion. For example, the suggestion unit customizes the suggestion content based on the user's current health condition and goals when making a suggestion. For example, if the user is on a diet, the suggestion unit can suggest a low-calorie menu. Also, if the user is aiming to build muscle, the suggestion unit can suggest a high-protein menu. For example, if the user has a specific health goal, the suggestion unit can suggest a menu that matches that goal. In this way, by customizing the suggestion content based on the user's health condition and goals, more appropriate menus can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's health condition and goal data into the generation AI and cause the generation AI to customize the suggestion content.

[0044] The suggestion unit can propose an optimal menu by taking into consideration the user's geographical location information when proposing a menu. For example, the suggestion unit proposes an optimal menu by taking into consideration the user's geographical location information when proposing a menu. For example, if the user is in a specific area, the suggestion unit proposes a menu using ingredients from that area. Furthermore, if the user is traveling, the suggestion unit can also propose a menu that takes into consideration the food culture of the travel destination. For example, if the user is at home, the suggestion unit proposes a menu using ingredients that can be purchased at a nearby supermarket. In this way, by taking into consideration the user's geographical location information, a more appropriate menu can be proposed. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's geographical location information into the generation AI and cause the generation AI to propose an optimal menu.

[0045] The suggestion unit can adjust the suggestion content by analyzing the user's social media activity when making a suggestion. For example, the suggestion unit can adjust the suggestion content by analyzing the user's social media activity when making a suggestion. For example, the suggestion unit makes suggestions based on meal information shared by the user on social media. The suggestion unit can also make suggestions based on recipes from cooking accounts the user follows. For example, the suggestion unit makes suggestions based on meal information that the user has "liked" on social media. This allows more appropriate menus to be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's social media data into a generation AI and have the generation AI adjust the suggestion content.

[0046] When coordinating with a delivery service, the collaboration unit can select the optimal service by referring to the user's past order history. For example, when coordinating with a delivery service, the collaboration unit selects the optimal service by referring to the user's past order history. For example, the collaboration unit selects the optimal service based on delivery services used by the user in the past. The collaboration unit can also select a service that provides a preferred menu from the user's past order history. For example, the collaboration unit analyzes the user's past order history and selects the service that provides the highest satisfaction. In this way, the optimal delivery service can be provided to the user by referring to the past order history. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's past order history data into the generation AI and cause the generation AI to select the optimal service.

[0047] The collaboration unit can customize the service content based on the user's current living situation when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit customizes the service content based on the user's current living situation. For example, if the user is busy, the collaboration unit selects a service that can deliver quickly. Also, if the user is relaxed, the collaboration unit can select a service that provides nutritionally balanced meals. For example, if the user has a specific health goal, the collaboration unit selects a service that provides meals that match that goal. This allows for customizing the service content based on the user's living situation to provide a more appropriate delivery service. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into a generation AI and cause the generation AI to customize the service content.

[0048] The collaboration unit can select the optimal service by taking into consideration the user's geographical location information when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit selects the optimal service by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the collaboration unit selects a delivery service available in that area. Furthermore, when the user is traveling, the collaboration unit can also select a delivery service available at the user's travel destination. For example, when the user is at home, the collaboration unit selects a nearby delivery service. This makes it possible to provide a more appropriate delivery service by taking into consideration the user's geographical location information. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal service.

[0049] The collaboration unit can adjust the service content by analyzing the user's social media activity when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit analyzes the user's social media activity and adjusts the service content. For example, the collaboration unit selects a delivery service based on meal information shared by the user on social media. The collaboration unit can also make a selection based on information about delivery services followed by the user. For example, the collaboration unit selects a delivery service based on meal information that the user has "liked" on social media. This makes it possible to provide a more appropriate delivery service by analyzing the user's social media activity. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI, for example. For example, the collaboration unit can input the user's social media data into a generation AI and have the generation AI adjust the service content.

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

[0051] When analyzing the user's dietary information, the evaluation unit can make an evaluation taking into account the user's past dietary patterns. For example, the evaluation unit evaluates the current nutritional balance based on nutrients that the user frequently consumed in the past. The evaluation can also take into account ingredients that the user avoided in the past. Furthermore, it is possible to predict deficiencies or excesses of specific nutrients from the user's past dietary patterns and reflect this in the evaluation. This makes it possible to make a more accurate evaluation of nutritional balance by taking into account the user's past dietary patterns.

[0052] When analyzing the user's dietary information, the evaluation unit can perform an evaluation based on the user's current health condition and goals. For example, if the user is on a diet, the evaluation can be performed with emphasis on calorie intake. Also, if the user is aiming to build muscle, the evaluation can be performed with emphasis on protein intake. Furthermore, if the user has a specific health goal, it is also possible to perform an evaluation with emphasis on the intake of nutrients that match that goal. This makes it possible to perform an evaluation based on the user's health condition and goals, thereby enabling a more appropriate evaluation of nutritional balance.

[0053] The suggestion unit can refer to the user's past meal history and make suggestions based on the menus that the user has previously enjoyed. For example, it can prioritize suggestions of menus that the user has frequently eaten in the past. It can also suggest menus that do not include ingredients that the user has avoided in the past. Furthermore, it can predict deficiencies or excesses of specific nutrients from the user's past meal history and suggest menus based on that. In this way, it is possible to suggest optimal menus to the user by referring to the past meal history.

[0054] The collaboration unit can select a delivery service based on the user's current living situation. For example, if the user is busy, a service that can deliver quickly can be selected. Also, if the user is relaxed, a service that provides nutritionally balanced meals can be selected. Furthermore, if the user has a specific health goal, it is also possible to select a service that provides meals that match that goal. In this way, by selecting a delivery service based on the user's living situation, more appropriate services can be provided.

[0055] The suggestion unit can suggest menus based on ingredients and dishes from the area where the user is currently located, taking into account the user's geographical location information. For example, if the user is in a specific area, it can suggest menus using ingredients from that area. Also, if the user is traveling, it can suggest menus that take into account the food culture of the travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients that can be purchased at a nearby supermarket. In this way, by taking into account the user's geographical location information, it is possible to suggest more appropriate menus.

[0056] The evaluation unit can evaluate nutritional balance by referring to literature related to the user. For example, the evaluation can be based on literature on nutrition that the user has read. The evaluation can also be based on research by nutritionists that the user follows. Furthermore, the evaluation can also be based on materials from nutrition seminars that the user has attended. In this way, by referring to related literature, the accuracy of the evaluation of nutritional balance can be improved.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input unit accepts meal information from the user. The meal information from the user includes detailed information such as the type and amount of food, and ingredients. The user can input the contents of the meals they ate in a day. For example, they might input information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner. Step 2: The evaluation unit evaluates the nutritional balance based on the dietary information received by the input unit. The evaluation unit retrieves the nutritional components of each ingredient from the database and compares them with the user's diet to calculate the nutritional balance. For example, it evaluates whether there are excesses or deficiencies of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. Step 3: The suggestion unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. The suggestion unit takes into consideration the user's preferences and allergy information and suggests menus such as yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner. Step 4: The collaboration unit collaborates with the delivery service based on the menu proposed by the proposal unit. Based on the proposed menu, the collaboration unit can order a lunch box from an affiliated delivery service and have it delivered to the user's home. The processing in the collaboration unit may be performed using AI, and a delivery service can be selected using an AI model that inputs the proposed menu and outputs the selection of a delivery service.

[0059] (Example 2) A nutritional balance suggestion system according to an embodiment of the present invention allows a user to input the meals they ate that day and proposes nutritionally balanced meal menus for the following day or the following day. This system also integrates with delivery services to deliver boxed lunches or pre-prepared meals for those who are not good at cooking. For example, a user inputs the meals they ate that day. This input includes detailed information such as the type, quantity, and ingredients of each meal. For example, input information such as "toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner." This information is then input into an AI. The AI ​​then analyzes the input meal information and evaluates the nutritional balance. The AI ​​retrieves the nutritional components of each ingredient from a database and compares them with the user's diet to calculate the nutritional balance. For example, it evaluates the excess or deficiency of nutrients such as vitamins, minerals, protein, carbohydrates, and lipids. The AI ​​then proposes a nutritionally balanced meal menu for the following day or the following day. For example, it suggests a menu such as "yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner." This suggestion also takes into account the user's preferences and allergies. Furthermore, for users who are not good at cooking at home, the system also has a function to deliver bento boxes or pre-prepared meals in cooperation with delivery services. For example, a bento box can be ordered from an affiliated delivery service based on a suggested menu and delivered to the user's home. At this time, the delivery service provides a nutritionally balanced meal based on the suggested menu. This system allows users to easily eat a nutritionally balanced meal. Furthermore, even people who are not good at cooking at home can easily enjoy healthy meals by using a delivery service. This system is extremely convenient for people who have difficulty cooking at home, such as busy businessmen and the elderly. In this way, the nutritional balance suggestion system allows users to easily eat a nutritionally balanced meal.

[0060] A nutritional balance suggestion system according to an embodiment includes an input unit, an evaluation unit, a suggestion unit, and a linking unit. The input unit accepts dietary information from a user. The dietary information from the user includes, but is not limited to, detailed information such as the type and amount of food and ingredients. The input unit, for example, inputs the contents of the meals the user ate in a day. For example, information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner can be input. The evaluation unit evaluates the nutritional balance based on the dietary information accepted by the input unit. For example, the evaluation unit obtains the nutritional components of each ingredient from a database and compares them with the user's dietary contents to calculate the nutritional balance. For example, the evaluation unit evaluates whether nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids are in excess or deficient. The suggestion unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. For example, the suggestion unit can propose a menu such as yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner. This suggestion also takes into account the user's preferences and allergy information. The collaboration unit collaborates with a delivery service based on the menu proposed by the proposal unit. For example, the collaboration unit can order a lunch box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. This allows the nutritional balance proposal system according to the embodiment to enable the user to easily eat a nutritionally balanced meal. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can select a delivery service using an AI model that receives the menu proposed by the proposal unit as input and outputs a selection of a delivery service.

[0061] The evaluation unit can obtain the nutritional components of each ingredient from the database and calculate the nutritional balance by comparing them with the user's dietary content. For example, the evaluation unit can obtain the nutritional components of each ingredient from the database and calculate the nutritional balance by comparing them with the user's dietary content. For example, the evaluation unit can evaluate excesses and deficiencies of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. The evaluation unit can also update the nutritional components of each ingredient in real time. For example, the evaluation unit can refer to the latest nutrition database and update the nutritional components of each ingredient. This enables accurate evaluation of nutritional balance by obtaining the nutritional components of each ingredient from the database. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI, or can be performed without AI. For example, the evaluation unit can evaluate nutritional balance using an AI model that inputs dietary information and outputs an evaluation of nutritional balance.

[0062] The suggestion unit can suggest a menu based on the user's preferences or allergy information. The suggestion unit, for example, suggests a menu taking into consideration the user's preferences and allergy information. For example, the suggestion unit suggests a menu based on the user's favorite ingredients and cooking methods. The suggestion unit can also suggest a menu that does not contain allergens based on the user's allergy information. For example, the suggestion unit suggests a menu that avoids ingredients to which the user is allergic. This makes it possible to suggest a more appropriate menu by taking into consideration the user's preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's preferences and allergy information and outputs menu suggestions.

[0063] The collaboration unit can order a bento box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. The collaboration unit can, for example, order a bento box from an affiliated delivery service based on the proposed menu and deliver it to the user's home. For example, the collaboration unit can select a delivery service that provides nutritionally balanced bento boxes based on the proposed menu. The collaboration unit can also select an appropriate delivery service taking into account the user's preferences and allergy information. For example, the collaboration unit can select a delivery service that provides bento boxes that avoid ingredients to which the user is allergic. This allows the user to easily eat a nutritionally balanced meal by collaborating with a delivery service based on the proposed menu. Some or all of the above-described processing by the collaboration unit can be performed, for example, using AI or without AI. For example, the collaboration unit can select a delivery service using an AI model that inputs the proposed menu and outputs a selection of a delivery service.

[0064] The input unit can input information about the types or amounts of meals and ingredients eaten by the user in a day. The input unit inputs, for example, detailed information about the types, amounts, and ingredients of meals eaten by the user in a day. For example, the input unit can input information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner. The input unit can also provide an interface that allows the user to easily input information about the types, amounts, and ingredients of meals. For example, the input unit may provide drop-down menus or check boxes that allow the user to input meal information simply by selecting it. This allows the user to input detailed meal information, enabling a more accurate evaluation of nutritional balance. Some or all of the above-described processing in the input unit may be performed, for example, using AI or without AI. For example, the input unit can input the meal information entered by the user to the generation AI and cause the generation AI to analyze the meal information.

[0065] The input unit can estimate the user's emotions and adjust the timing of inputting meal information based on the estimated user emotions. The input unit, for example, estimates the user's emotions and adjusts the timing of inputting meal information based on the estimated user emotions. For example, if the user is feeling stressed, the input unit prompts the user to input meal information during a time when the user is able to relax. The input unit can also provide a simple interface that allows the user to input information in a short time when the user is busy. For example, if the user is relaxed, the input unit prompts the user to input detailed information, thereby improving the accuracy of the meal information. This allows the input timing to be adjusted according to the user's emotions, allowing the meal information to be input at a more appropriate time. Emotion estimation is achieved 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The input unit can analyze the user's past meal history and select the optimal input method. The input unit, for example, analyzes the user's past meal history and selects the optimal input method. For example, the input unit automatically displays meal information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit predicts and suggests meal information to be input during a specific time period based on the user's past meal history. In this way, by analyzing the past meal history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past meal history data to the generation AI and cause the generation AI to select the optimal input method.

[0067] The input unit can filter the dietary information based on the user's current health condition and goals when inputting the dietary information. For example, when inputting the dietary information, the input unit filters the dietary information based on the user's current health condition and goals. For example, if the user is on a diet, the input unit filters out high-calorie dietary information. Furthermore, if the user is aiming to build muscle, the input unit can preferentially display high-protein dietary information. For example, if the user has a specific health goal, the input unit suggests dietary information that matches the goal. This allows more appropriate dietary information to be input by filtering based on the user's health condition and goals. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's health condition and goal data to the generation AI and have the generation AI perform the filtering.

[0068] The input unit can estimate the user's emotions and determine the priority of the meal information to be input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of the meal information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the input unit prioritizes displaying meal information that can be easily input. The input unit can also prioritize input of detailed meal information when the user is relaxed. For example, when the user is in a hurry, the input unit prioritizes input of only important meal information. This allows more appropriate meal information to be input by determining the priority of the meal information to be input based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using an AI, for example, or without an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0069] When inputting meal information, the input unit can prioritize inputting highly relevant meal information taking into account the user's geographical location information. For example, when inputting meal information, the input unit prioritizes inputting highly relevant meal information taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting ingredients and dishes from that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting meal information from the travel destination. For example, when the user is at home, the input unit can prioritize inputting ingredients that can be purchased at a nearby supermarket. In this way, more relevant meal information can be input by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant meal information.

[0070] The input unit can analyze the user's social media activity when inputting meal information and input related meal information. For example, the input unit can analyze the user's social media activity when inputting meal information and input related meal information. For example, the input unit can automatically input meal information shared by the user on social media. The input unit can also prioritize input of recipes from cooking accounts the user follows. For example, the input unit can input meal information that the user has "liked" on social media. This allows more relevant meal information to be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's social media data to the generation AI and cause the generation AI to select related meal information.

[0071] The evaluation unit can estimate the user's emotions and adjust the nutritional balance evaluation method based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions and adjusts the nutritional balance evaluation method based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit may prioritize nutrients effective for stress reduction. Furthermore, if the user is relaxed, the evaluation unit may prioritize overall nutritional balance. For example, if the user is tired, the evaluation unit may prioritize nutrients effective for energy replenishment. This allows for a more appropriate evaluation by adjusting the nutritional balance evaluation method according to the user's emotions. The emotion estimation is achieved 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 evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the nutritional balance evaluation method.

[0072] The evaluation unit can improve the accuracy of the evaluation when evaluating nutritional balance by referring to the user's past health data. For example, the evaluation unit can improve the accuracy of the evaluation when evaluating nutritional balance by referring to the user's past health data. For example, the evaluation unit can evaluate nutritional balance by referring to the user's past blood test results. The evaluation unit can also evaluate nutritional balance by referring to the user's past diet history. For example, the evaluation unit can evaluate nutritional balance by referring to the user's past exercise data. By referring to the past health data, the accuracy of the nutritional balance evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the user's past health data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0073] The evaluation unit can take into account the user's lifestyle habits and amount of exercise when evaluating nutritional balance. For example, the evaluation unit can take into account the user's lifestyle habits and amount of exercise when evaluating nutritional balance. For example, if the user exercises frequently, the evaluation unit can evaluate by focusing on protein intake. Furthermore, if the user lives a desk-based lifestyle, the evaluation unit can also evaluate by focusing on vitamin and mineral intake. For example, if the user works night shifts, the evaluation unit can evaluate by focusing on nutrients that are effective for energy replenishment. This enables a more appropriate evaluation of nutritional balance by taking the user's lifestyle habits and amount of exercise into consideration. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input data on the user's lifestyle habits and amount of exercise into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0074] The evaluation unit can estimate the user's emotions and adjust the display order of the nutritional balance evaluation results based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions and adjusts the display order of the nutritional balance evaluation results based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit may first display the evaluation results of nutrients effective for stress reduction. Furthermore, if the user is relaxed, the evaluation unit may first display the evaluation results of overall nutritional balance. For example, if the user is tired, the evaluation unit may first display the evaluation results of nutrients effective for energy replenishment. This allows for adjusting the display order of the evaluation results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved 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 evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the evaluation results.

[0075] The evaluation unit can take the user's geographical distribution into consideration when evaluating the nutritional balance. For example, the evaluation unit can evaluate the nutritional balance by taking the user's geographical distribution into consideration when evaluating the nutritional balance. For example, the evaluation unit can evaluate the nutritional balance by taking the food culture of the area where the user lives into consideration. Furthermore, if the user is traveling, the evaluation unit can also evaluate the nutritional balance by taking the food culture of the travel destination into consideration. For example, if the user is in a specific area, the evaluation unit can evaluate the nutritional balance by taking the ingredients of that area into consideration. This allows for a more appropriate evaluation of the nutritional balance by taking the user's geographical distribution into consideration. Some or all of the above-described processing by the evaluation unit can be performed using, or without, AI, for example. For example, the evaluation unit can input the user's geographical distribution data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0076] The evaluation unit can improve the accuracy of the evaluation by referring to the user's related literature when evaluating nutritional balance. For example, the evaluation unit improves the accuracy of the evaluation by referring to the user's related literature when evaluating nutritional balance. For example, the evaluation unit makes the evaluation by referring to nutrition-related literature read by the user. The evaluation unit can also make the evaluation by referring to research by nutritionists the user follows. For example, the evaluation unit makes the evaluation by referring to materials from nutrition seminars attended by the user. By doing so, the accuracy of the nutritional balance evaluation is improved by referring to the related literature. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit may input the user's related literature data into the generation AI and cause the generation AI to evaluate the nutritional balance.

[0077] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit provides simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit provides quick suggestions that focus on the main points. This allows for more appropriate suggestions by adjusting the way the suggestions are expressed based on the user's emotions. The emotion estimation is achieved 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0078] The suggestion unit can suggest an optimal menu by referring to the user's past meal history when making a suggestion. For example, the suggestion unit suggests an optimal menu by referring to the user's past meal history when making a suggestion. For example, the suggestion unit makes a suggestion based on menus that the user has previously preferred. The suggestion unit can also suggest a nutritionally balanced menu from the user's past meal history. For example, the suggestion unit analyzes the user's past meal history and suggests the most suitable menu. In this way, the optimal menu can be suggested to the user by referring to the past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past meal history data into the generation AI and cause the generation AI to suggest an optimal menu.

[0079] The suggestion unit can customize the suggestion content based on the user's current health condition and goals when making a suggestion. For example, the suggestion unit customizes the suggestion content based on the user's current health condition and goals when making a suggestion. For example, if the user is on a diet, the suggestion unit can suggest a low-calorie menu. Also, if the user is aiming to build muscle, the suggestion unit can suggest a high-protein menu. For example, if the user has a specific health goal, the suggestion unit can suggest a menu that matches that goal. In this way, by customizing the suggestion content based on the user's health condition and goals, more appropriate menus can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's health condition and goal data into the generation AI and cause the generation AI to customize the suggestion content.

[0080] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting menus that have a relaxing effect. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggesting nutritionally balanced menus. For example, if the user is in a hurry, the suggestion unit can prioritize suggesting easy-to-prepare menus. This allows for suggesting more appropriate menus by prioritizing suggestions based on the user's emotions. The 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0081] The suggestion unit can propose an optimal menu by taking into consideration the user's geographical location information when proposing a menu. For example, the suggestion unit proposes an optimal menu by taking into consideration the user's geographical location information when proposing a menu. For example, if the user is in a specific area, the suggestion unit proposes a menu using ingredients from that area. Furthermore, if the user is traveling, the suggestion unit can also propose a menu that takes into consideration the food culture of the travel destination. For example, if the user is at home, the suggestion unit proposes a menu using ingredients that can be purchased at a nearby supermarket. In this way, by taking into consideration the user's geographical location information, a more appropriate menu can be proposed. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's geographical location information into the generation AI and cause the generation AI to propose an optimal menu.

[0082] The suggestion unit can adjust the suggestion content by analyzing the user's social media activity when making a suggestion. For example, the suggestion unit can adjust the suggestion content by analyzing the user's social media activity when making a suggestion. For example, the suggestion unit makes suggestions based on meal information shared by the user on social media. The suggestion unit can also make suggestions based on recipes from cooking accounts the user follows. For example, the suggestion unit makes suggestions based on meal information that the user has "liked" on social media. This allows more appropriate menus to be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's social media data into a generation AI and have the generation AI adjust the suggestion content.

[0083] The collaboration unit can estimate a user's emotions and select a delivery service based on the estimated user's emotions. For example, the collaboration unit can estimate a user's emotions and select a delivery service based on the estimated user's emotions. For example, if a user is feeling stressed, the collaboration unit can select a delivery service that provides meals that have a relaxing effect. Furthermore, if a user is relaxed, the collaboration unit can select a delivery service that provides nutritionally balanced meals. For example, if a user is in a hurry, the collaboration unit can select a service that can deliver quickly. This allows for the selection of a delivery service based on the user's emotions, thereby providing a more appropriate service. Emotion estimation is achieved using an emotion estimation function, for example, using 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 collaboration unit can be performed using an AI, for example, or without an AI. For example, the collaboration unit can input user's emotion data into the generation AI and cause the generation AI to select a delivery service.

[0084] When coordinating with a delivery service, the collaboration unit can select the optimal service by referring to the user's past order history. For example, when coordinating with a delivery service, the collaboration unit selects the optimal service by referring to the user's past order history. For example, the collaboration unit selects the optimal service based on delivery services used by the user in the past. The collaboration unit can also select a service that provides a preferred menu from the user's past order history. For example, the collaboration unit analyzes the user's past order history and selects the service that provides the highest satisfaction. In this way, the optimal delivery service can be provided to the user by referring to the past order history. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input the user's past order history data into the generation AI and cause the generation AI to select the optimal service.

[0085] The collaboration unit can customize the service content based on the user's current living situation when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit customizes the service content based on the user's current living situation. For example, if the user is busy, the collaboration unit selects a service that can deliver quickly. Also, if the user is relaxed, the collaboration unit can select a service that provides nutritionally balanced meals. For example, if the user has a specific health goal, the collaboration unit selects a service that provides meals that match that goal. This allows for customizing the service content based on the user's living situation to provide a more appropriate delivery service. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into a generation AI and cause the generation AI to customize the service content.

[0086] The collaboration unit can estimate a user's emotions and prioritize delivery services based on the estimated user emotions. The collaboration unit, for example, estimates a user's emotions and prioritizes delivery services based on the estimated user emotions. For example, if a user is feeling stressed, the collaboration unit may prioritize services that provide meals with a relaxing effect. Furthermore, if a user is relaxed, the collaboration unit may prioritize services that provide nutritionally balanced meals. For example, if a user is in a hurry, the collaboration unit may prioritize services that can deliver quickly. This allows for more appropriate services to be provided by prioritizing delivery services based on the user's emotions. The emotion estimation is achieved 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 collaboration unit may be performed using, for example, an AI. For example, the collaboration unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of delivery services.

[0087] The collaboration unit can select the optimal service by taking into consideration the user's geographical location information when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit selects the optimal service by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the collaboration unit selects a delivery service available in that area. Furthermore, when the user is traveling, the collaboration unit can also select a delivery service available at the user's travel destination. For example, when the user is at home, the collaboration unit selects a nearby delivery service. This makes it possible to provide a more appropriate delivery service by taking into consideration the user's geographical location information. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal service.

[0088] The collaboration unit can adjust the service content by analyzing the user's social media activity when collaborating with a delivery service. For example, when collaborating with a delivery service, the collaboration unit analyzes the user's social media activity and adjusts the service content. For example, the collaboration unit selects a delivery service based on meal information shared by the user on social media. The collaboration unit can also make a selection based on information about delivery services followed by the user. For example, the collaboration unit selects a delivery service based on meal information that the user has "liked" on social media. This makes it possible to provide a more appropriate delivery service by analyzing the user's social media activity. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI, for example. For example, the collaboration unit can input the user's social media data into a generation AI and have the generation AI adjust the service content. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, evaluation unit, suggestion unit, and linking 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 accepts meal information from the user using the reception device 38 of the smart device 14. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, obtains the nutritional components of each ingredient from the database 24 and compares them with the user's meal content to calculate the nutritional balance. The suggestion unit, realized by the specific processing unit 290 of the data processing device 12, suggests a meal menu for the next day or the following day based on the evaluated nutritional balance. The linking unit, realized by the control unit 46A of the smart device 14, collaborates with a delivery service based on the suggested menu and delivers meals to the user's home. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, evaluation unit, suggestion unit, and collaboration 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 receives dietary information from the user using the microphone 238 of the smart glasses 214. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, obtains the nutritional components of each ingredient from the database 24, and compares them with the user's diet to calculate nutritional balance. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests a meal menu for the next day or the following day based on the evaluated nutritional balance. The collaboration unit is realized by the control unit 46A of the smart glasses 214, and collaborates with a delivery service based on the suggested menu to deliver meals to the user's home. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, evaluation unit, suggestion unit, and linking unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit receives meal information from the user using the microphone 238 of the headset-type terminal 314. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, obtains the nutritional components of each ingredient from the database 24, and compares them with the user's diet to calculate the nutritional balance. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests meal menus for the next day or the following day based on the evaluated nutritional balance. The linking unit is realized by the control unit 46A of the headset-type terminal 314, and links with a delivery service based on the suggested menu to deliver meals to the user's home. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, evaluation unit, suggestion unit, and collaboration 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 receives meal information from the user using the microphone 238 of the robot 414. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, obtains the nutritional components of each ingredient from the database 24, and compares them with the user's meal content to calculate the nutritional balance. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests meal menus for the next day or the following day based on the evaluated nutritional balance. The collaboration unit is realized by the control unit 46A of the robot 414, and collaborates with a delivery service based on the suggested menu to deliver meals to the user's home.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The suggestion unit can estimate the user's emotions and adjust the difficulty level of the suggested menu based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest a menu that is easy to make. Also, if the user is relaxed, it can suggest a menu that requires a little effort. Furthermore, if the user is busy, it can suggest a menu that can be made in a short amount of time. In this way, by adjusting the difficulty level of the menu according to the user's emotions, more appropriate suggestions can be made.

[0091] When analyzing the user's dietary information, the evaluation unit can make an evaluation taking into account the user's past dietary patterns. For example, the evaluation unit evaluates the current nutritional balance based on nutrients that the user frequently consumed in the past. The evaluation can also take into account ingredients that the user avoided in the past. Furthermore, it is possible to predict deficiencies or excesses of specific nutrients from the user's past dietary patterns and reflect this in the evaluation. This makes it possible to make a more accurate evaluation of nutritional balance by taking into account the user's past dietary patterns.

[0092] The suggestion unit can estimate the user's emotions and adjust the variations in the menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a simple, standard menu can be suggested. If the user is relaxed, a menu incorporating new ingredients or cooking methods can be suggested. Furthermore, if the user is adventurous, it can suggest exotic dishes or menus using unusual ingredients. This allows for more appropriate suggestions by adjusting the variations in the menu according to the user's emotions.

[0093] The linking unit can estimate the user's emotions and select a delivery service based on the estimated user's emotions. For example, if the user is feeling stressed, a delivery service that provides meals with a relaxing effect can be selected. Also, if the user is relaxed, a delivery service that provides nutritionally balanced meals can be selected. Furthermore, if the user is in a hurry, a service that can deliver quickly can be selected. In this way, by selecting a delivery service according to the user's emotions, more appropriate services can be provided.

[0094] The input unit can estimate the user's emotions and determine the priority of the meal information to be input based on the estimated user's emotions. For example, if the user is feeling stressed, meal information that can be easily input can be displayed preferentially. Also, if the user is relaxed, detailed meal information can be input preferentially. Furthermore, if the user is in a hurry, it is also possible to input only important meal information preferentially. In this way, by determining the priority of the meal information to be input according to the user's emotions, more appropriate meal information can be input.

[0095] When analyzing the user's dietary information, the evaluation unit can perform an evaluation based on the user's current health condition and goals. For example, if the user is on a diet, the evaluation can be performed with emphasis on calorie intake. Also, if the user is aiming to build muscle, the evaluation can be performed with emphasis on protein intake. Furthermore, if the user has a specific health goal, it is also possible to perform an evaluation with emphasis on the intake of nutrients that match that goal. This makes it possible to perform an evaluation based on the user's health condition and goals, thereby enabling a more appropriate evaluation of nutritional balance.

[0096] The suggestion unit can refer to the user's past meal history and make suggestions based on the menus that the user has previously enjoyed. For example, it can prioritize suggestions of menus that the user has frequently eaten in the past. It can also suggest menus that do not include ingredients that the user has avoided in the past. Furthermore, it can predict deficiencies or excesses of specific nutrients from the user's past meal history and suggest menus based on that. In this way, it is possible to suggest optimal menus to the user by referring to the past meal history.

[0097] The collaboration unit can select a delivery service based on the user's current living situation. For example, if the user is busy, a service that can deliver quickly can be selected. Also, if the user is relaxed, a service that provides nutritionally balanced meals can be selected. Furthermore, if the user has a specific health goal, it is also possible to select a service that provides meals that match that goal. In this way, by selecting a delivery service based on the user's living situation, more appropriate services can be provided.

[0098] The suggestion unit can suggest menus based on ingredients and dishes from the area where the user is currently located, taking into account the user's geographical location information. For example, if the user is in a specific area, it can suggest menus using ingredients from that area. Also, if the user is traveling, it can suggest menus that take into account the food culture of the travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients that can be purchased at a nearby supermarket. In this way, by taking into account the user's geographical location information, it is possible to suggest more appropriate menus.

[0099] The evaluation unit can evaluate nutritional balance by referring to literature related to the user. For example, the evaluation can be based on literature on nutrition that the user has read. The evaluation can also be based on research by nutritionists that the user follows. Furthermore, the evaluation can also be based on materials from nutrition seminars that the user has attended. In this way, by referring to related literature, the accuracy of the evaluation of nutritional balance can be improved.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The input unit accepts meal information from the user. The meal information from the user includes detailed information such as the type and amount of food, and ingredients. The user can input the contents of the meals they ate in a day. For example, they might input information such as toast and eggs for breakfast, salad and chicken for lunch, and pasta and salad for dinner. Step 2: The evaluation unit evaluates the nutritional balance based on the dietary information received by the input unit. The evaluation unit retrieves the nutritional components of each ingredient from the database and compares them with the user's diet to calculate the nutritional balance. For example, it evaluates whether there are excesses or deficiencies of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. Step 3: The suggestion unit proposes a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit. The suggestion unit takes into consideration the user's preferences and allergy information and suggests menus such as yogurt and fruit for breakfast, fish and vegetables for lunch, and tofu and stir-fried vegetables for dinner. Step 4: The collaboration unit collaborates with the delivery service based on the menu proposed by the proposal unit. Based on the proposed menu, the collaboration unit can order a lunch box from an affiliated delivery service and have it delivered to the user's home. The processing in the collaboration unit may be performed using AI, and a delivery service can be selected using an AI model that inputs the proposed menu and outputs the selection of a delivery service.

[0102] 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.

[0103] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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, in order to avoid confusion and to 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 unit that accepts meal information from a user; an evaluation unit that evaluates nutritional balance based on the dietary information received by the input unit; a suggestion unit that suggests a meal menu for the next day or the following day based on the nutritional balance evaluated by the evaluation unit; a linking unit that links with a delivery service based on the menu proposed by the suggestion unit; A system characterized by:

2. The evaluation unit The nutritional information for each ingredient is retrieved from the database and compared with the user's diet to calculate the nutritional balance.

2. The system of claim 1.

3. The proposal unit Suggest menu items based on user preferences or allergies 2. The system of claim 1.

4. The linking unit is Order a lunch box from a partner delivery service based on the suggested menu and have it delivered to the user's home 2. The system of claim 1.

5. The input unit The user inputs the type or amount of food eaten per day and information on ingredients.

2. The system of claim 1.

6. The input unit The system estimates the user's emotions and adjusts the timing of meal information input based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyze the user's past meal history and select the optimal input method 2. The system of claim 1.

8. The input unit Filtering dietary information based on the user's current health status and goals 2. The system of claim 1.

Images