System

Abstract

To provide a system according to an embodiment that identifies a proper nutrient on the basis of subjective symptom of a user, and proposes a meal.SOLUTION: A system according to an embodiment comprises: a reception unit; an identification unit; and a proposal unit. The reception unit is configured to input subjective symptoms of a user. The identification unit is configured to identify a nutrient on the basis of the subjective symptom inputted by the reception unit. The proposal unit is configured to propose a meal on the basis of the identified nutrient.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that they have not adequately identified appropriate nutrients and suggested meals based on a user's subjective symptoms.

[0005] The system according to the embodiment aims to identify appropriate nutrients based on the user's subjective symptoms and to suggest meals. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an identifying unit, and a suggestion unit. The receiving unit inputs a subjective symptom of a user. The identifying unit identifies nutrients based on the subjective symptom input by the receiving unit. The suggestion unit suggests a meal based on the nutrients identified by the identifying unit. Effect of the Invention

[0007] The system according to the embodiment can identify appropriate nutrients based on the user's subjective symptoms and suggest meals. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system that identifies nutrients that may be lacking in the body based on the discomfort or subjective symptoms felt by the user and suggests appropriate meals. In this AI system, the user inputs subjective symptoms, and the AI ​​analyzes the information to identify the nutrients that are lacking and suggests meals. For example, if the user inputs symptoms such as "easily tired," "easily stiff shoulders," and "frequent headaches," the AI ​​identifies that there is a possibility of a deficiency in iron or B vitamins based on the information. Next, the AI ​​suggests meals to supplement the identified nutrients. The suggestions vary depending on where the user eats. For example, if the user eats at a convenience store or chain restaurant, the AI ​​suggests recommended menus in cooperation with menu information. In addition, if the user selects to cook at home, the AI ​​suggests recipes. In addition, the AI ​​provides nutrient information for the suggested meals. For example, details of the nutrients contained in the proposed menus and recipes are displayed so that the user can check which nutrients can be ingested. This allows the user to choose an appropriate meal that matches their physical condition. For example, a user who feels easily tired can reduce fatigue by following the AI's suggestions and eating meals rich in iron and B vitamins. Additionally, users who tend to suffer from stiff shoulders can reduce their stiffness by following the AI's suggestions and eating meals rich in magnesium and vitamin E. This allows the AI ​​system to identify appropriate nutrients and suggest meals based on the user's subjective symptoms.

[0029] The AI ​​system according to the embodiment includes a reception unit, an identification unit, and a suggestion unit. The reception unit inputs a subjective symptom of a user. The subjective symptoms of the user include, for example, headache, fatigue, stomach discomfort, etc., but are not limited to such examples. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. The identification unit uses AI to identify nutrients based on the subjective symptoms input by the reception unit. The nutrients include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. The identification unit identifies nutrients based on, for example, past data and medical knowledge by the AI. The identification unit can also identify nutrients by referring to the user's past diet history and health checkup results by the AI. The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. The meals include, for example, breakfast, lunch, dinner, snacks, etc., but are not limited to such examples. For example, when the user eats at a convenience store or a chain restaurant, the suggestion unit suggests a recommended menu in cooperation with menu information. In addition, the suggestion unit can also suggest recipes when the user selects home cooking. This allows the AI ​​system according to the embodiment to identify appropriate nutrients based on the user's subjective symptoms and suggest meals.

[0030] The reception unit inputs the subjective symptoms of the user. The subjective symptoms of the user include, for example, headache, fatigue, stomach discomfort, etc., but are not limited to such examples. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. Specifically, the user can input the subjective symptoms in text format through a dedicated application or web interface of the smartphone or personal computer. In the case of voice input, the user's utterance is converted into text data using voice recognition technology and imported into the system. In the case of using a touch panel, the user can easily input the subjective symptoms by tapping on an option on the screen. Furthermore, the reception unit can temporarily store the user's input data and refer to the past input history as necessary. This makes it possible for the user to recheck the subjective symptoms entered in the past or to quickly respond if a similar symptom recurs. In addition, the reception unit encrypts and stores the user's input data, and takes security measures to protect privacy. This allows the user to input the subjective symptoms with peace of mind.

[0031] The identification unit uses AI to identify nutrients based on subjective symptoms input by the reception unit. Examples of nutrients include, but are not limited to, vitamins, minerals, and proteins. For example, the identification unit uses AI to identify nutrients based on past data and medical knowledge. In addition, the identification unit can also identify nutrients by referring to the user's past dietary history and health checkup results. Specifically, the AI ​​uses natural language processing technology to analyze the subjective symptoms input by the user and identify related nutrients. For example, if a headache symptom is input, the AI ​​considers the possibility that it may be caused by a lack of vitamin B2 or magnesium. In addition, if a feeling of fatigue is input, a lack of iron or vitamin B12 may be considered. Furthermore, the AI ​​refers to a large-scale medical database and research papers to identify nutrients based on the latest medical knowledge. When referring to the user's past dietary history and health checkup results, the AI ​​performs an integrated analysis of these data and comprehensively evaluates the user's nutritional state. For example, if it is found from the past dietary history that a specific nutrient is lacking, a suggestion is made to supplement that nutrient. In addition, if the health checkup results indicate an excessive intake of a particular nutrient, the identification unit will suggest that the user refrain from taking in that nutrient. This allows the identification unit to identify optimal nutrients according to the individual situation of the user, thereby contributing to the maintenance and improvement of health.

[0032] The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. Meals include, for example, breakfast, lunch, dinner, and snacks, but are not limited to such examples. For example, when the user eats at a convenience store or chain restaurant, the suggestion unit suggests recommended menus in cooperation with menu information. In addition, when the user selects home cooking, the suggestion unit can also suggest recipes. Specifically, the AI ​​suggests optimal meal menus based on the identified nutrients, taking into consideration the user's preferences and allergy information. For example, if the user is deficient in vitamin C, the AI ​​suggests menus using fruits and vegetables that are rich in vitamin C. When the user eats at a convenience store or chain restaurant, the AI ​​obtains the store's menu information in real time and selects menus with balanced nutrients. For example, a specific salad or sandwich may be suggested. When the user selects home cooking, the AI ​​suggests specific recipes and provides detailed explanations of the necessary ingredients and cooking procedures. In addition, the suggestion unit records the user's meal history and improves the accuracy of the suggestions based on past suggestions and user feedback. For example, if the user likes a menu that was suggested in the past, a similar menu can be suggested again. The suggestion unit also analyzes the user's diet history, detects imbalances in nutritional balance, and makes suggestions to adjust dietary content as necessary, thereby making it possible for the suggestion unit to make specific dietary suggestions to maintain and improve the user's health condition.

[0033] The system includes a providing unit that provides nutrient information of the proposed meal. The providing unit provides the nutrient information of the proposed meal to the user. The nutrient information includes, for example, nutrient content, health effects, and the like, but is not limited to such examples. The providing unit, for example, displays details of nutrients contained in a proposed menu or recipe, allowing the user to confirm which nutrients can be ingested. The providing unit can also display the nutrient information graphically. For example, the nutrient content is displayed in a graph or chart, and provided in a visually easy-to-understand format. This makes it possible to provide the nutrient information of the proposed meal to the user.

[0034] The device includes a linking unit that links with menu information of convenience stores or chain stores. The linking unit links with menu information of convenience stores or chain stores. Menu information includes, for example, dish names, nutritional components, prices, etc., but is not limited to these examples. The linking unit, for example, obtains menu information of convenience stores or chain stores in real time and provides it to the user. The linking unit can also analyze the menu information using AI and suggest the most suitable menu to the user. This makes it possible to suggest appropriate menus in collaboration with menu information of convenience stores or chain stores.

[0035] The device includes a recipe unit that suggests recipes when home cooking is selected. The recipe unit suggests recipes when home cooking is selected. Recipes include, but are not limited to, ingredients, cooking steps, and cooking times. For example, when a user selects home cooking, the recipe unit uses AI to suggest an appropriate recipe. The recipe unit can also provide nutritional information for the suggested recipe. This makes it possible to suggest appropriate recipes when home cooking is selected.

[0036] The identification unit can identify nutrients based on past data or medical knowledge. The identification unit uses AI to identify nutrients based on past data or medical knowledge. Past data includes, for example, past dietary history, health checkup results, etc., but is not limited to such examples. Medical knowledge includes, for example, nutritional knowledge, and the relationship between symptoms of illness and nutrients, but is not limited to such examples. For example, the identification unit uses AI to analyze past dietary history and identify nutrients that are currently lacking. The identification unit can also identify nutrients related to the user's subjective symptoms based on AI medical knowledge. This makes it possible to make highly accurate suggestions by identifying nutrients based on past data and medical knowledge.

[0037] The suggestion unit can change the suggestion content depending on where the user eats. The suggestion unit uses AI to change the suggestion content depending on where the user eats. The eating place includes, but is not limited to, for example, a home, a restaurant, and a workplace. For example, when the user eats at home, the suggestion unit makes a recipe suggestion. In addition, when the user eats at a restaurant, the suggestion unit can also suggest a recommended menu in cooperation with menu information. This allows more appropriate suggestions to be made by changing the suggestion content depending on where the user eats.

[0038] The reception unit can analyze the user's past subjective symptom history and select an appropriate input method. The reception unit uses AI to analyze the user's past subjective symptom history and select an appropriate input method. The past subjective symptom history includes, for example, past symptom records, frequency, duration, etc., but is not limited to such examples. The reception unit, for example, automatically displays subjective symptoms that the user frequently inputs in the past as candidates. In addition, the reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest subjective symptoms to be input in a specific time period from the user's past subjective symptom history. In this way, the optimal input method can be selected by analyzing the user's past subjective symptom history.

[0039] The reception unit can perform filtering based on the user's current living situation or field of interest when inputting subjective symptoms. The reception unit uses AI to perform filtering based on the user's current living situation or field of interest when inputting subjective symptoms. The current living situation includes, for example, work situation, home environment, lifestyle, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, health interests, etc., but is not limited to such examples. For example, when the user is at work, the reception unit preferentially inputs subjective symptoms related to work. In addition, when the user is on vacation, the reception unit can also preferentially input subjective symptoms related to relaxation. Furthermore, when the user is exercising, the reception unit can also preferentially input subjective symptoms related to exercise. In this way, by filtering based on the user's current living situation and field of interest, it is possible to encourage the input of more appropriate subjective symptoms.

[0040] The reception unit can preferentially input highly relevant symptoms based on the geographical location information of the user when inputting subjective symptoms. The reception unit uses AI to preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting subjective symptoms. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is at high altitude, the reception unit preferentially inputs subjective symptoms related to high altitude. In addition, when the user is in an urban area, the reception unit can also preferentially input subjective symptoms related to urban areas. Furthermore, when the user is at the seaside, the reception unit can also preferentially input subjective symptoms related to the seaside. As a result, it is possible to encourage the input of more appropriate subjective symptoms by preferentially inputting highly relevant symptoms in consideration of the geographical location information of the user.

[0041] The reception unit can analyze the user's social media activity and input related symptoms when the subjective symptoms are input. The reception unit uses AI to analyze the user's social media activity and input related symptoms when the subjective symptoms are input. Social media activity includes, for example, the content of the post, the number of likes, comments, and the like, but is not limited to such examples. For example, when the user posts about fatigue on social media, the reception unit preferentially inputs subjective symptoms related to fatigue. In addition, when the user posts about stress on social media, the reception unit can also preferentially input subjective symptoms related to stress. Furthermore, when the user posts about health on social media, the reception unit can also preferentially input subjective symptoms related to health. In this way, the input of related subjective symptoms can be prompted by analyzing the user's social media activity.

[0042] The identification unit can improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The identification unit identifies nutrients that are currently lacking based on nutrients taken by the user in the past. The identification unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history to improve the accuracy of identification. Furthermore, the identification unit can also improve the accuracy of identification by referring to the user's past nutritional intake history and predicting the intake amount of a specific nutrient. Thereby, the accuracy of identification can be improved by referring to the user's past nutritional intake history.

[0043] The identification unit can customize the specific means based on the user's current living situation when identifying nutrients. The identification unit uses AI to customize the specific means based on the user's current living situation when identifying nutrients. The current living situation includes, but is not limited to, for example, work situation, home environment, and lifestyle. For example, when the user is at work, the identification unit preferentially identifies nutrients related to work. In addition, when the user is on vacation, the identification unit can also preferentially identify nutrients related to relaxation. Furthermore, when the user is exercising, the identification unit can also preferentially identify nutrients related to exercise. This makes it possible to identify more appropriate nutrients by customizing the specific means based on the user's current living situation.

[0044] The identification unit can identify appropriate nutrients based on the geographical location information of the user when identifying nutrients. The identification unit uses AI to identify optimal nutrients taking into account the geographical location information of the user when identifying nutrients. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. For example, when the user is in a highland, the identification unit preferentially identifies nutrients related to highland. In addition, when the user is in an urban area, the identification unit can also preferentially identify nutrients related to urban areas. Furthermore, when the user is on the seashore, the identification unit can also preferentially identify nutrients related to the seashore. This allows more appropriate nutrients to be identified by identifying optimal nutrients taking into account the geographical location information of the user.

[0045] The identification unit can improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, when a user posts about fatigue on social media, the identification unit preferentially identifies nutrients related to fatigue. In addition, when a user posts about stress on social media, the identification unit can also preferentially identify nutrients related to stress. Furthermore, when a user posts about health on social media, the identification unit can also preferentially identify nutrients related to health. This makes it possible to improve the accuracy of identification by analyzing the user's social media activity.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the nutrient using AI at the time of suggestion. The importance of a nutrient includes, for example, the degree of impact on health and the necessity of intake, but is not limited to such examples. For example, the suggestion unit makes a proposal including detailed information regarding important nutrients. In addition, the suggestion unit can also make a proposal including concise information regarding nutrients that are not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the nutrient. As a result, a more appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the nutrient.

[0047] The suggestion unit can apply different suggestion algorithms depending on the nutrient category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the nutrient category at the time of suggestion. Nutrient categories include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. For example, the suggestion unit applies an algorithm that takes into account the absorption rate of vitamins to a suggestion regarding vitamins. In addition, the suggestion unit can also apply an algorithm that takes into account the interaction of minerals to a suggestion regarding minerals. Furthermore, the suggestion unit can apply an algorithm that takes into account the digestion and absorption of proteins to a proposal regarding proteins. In this way, more appropriate suggestions can be made by applying the optimal suggestion algorithm according to the nutrient category.

[0048] The suggestion unit can determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The time when the nutrient was specified includes, but is not limited to, the season, the health condition of the user, and the like. For example, if the time when the nutrient was specified is recent, the suggestion unit can preferentially suggest the nutrient. In addition, if the time when the nutrient was specified is in the past, the suggestion unit can also lower the priority of the nutrient. Furthermore, the suggestion unit can gradually adjust the priority of the suggestion based on the time when the nutrient was specified. In this way, more appropriate suggestions can be made by determining the priority of the suggestion based on the time when the nutrient was specified.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. The suggestion unit adjusts the order of suggestions based on the relevance of nutrients using AI at the time of suggestion. Nutrient relevance includes, for example, interactions and synergistic effects, but is not limited to such examples. For example, the suggestion unit preferentially suggests nutrients with high relevance. In addition, the suggestion unit can postpone the suggestion order of nutrients with low relevance. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of nutrients. Thereby, by adjusting the order of suggestions based on the relevance of nutrients, more appropriate suggestions can be made.

[0050] The providing unit can provide appropriate information based on the user's past nutritional intake history when providing nutrient information. The providing unit uses AI to provide optimal information by referring to the user's past nutritional intake history when providing nutrient information. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The providing unit provides information on nutrients currently lacking based on nutrients taken by the user in the past, for example. The providing unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history and provide optimal information. Furthermore, the providing unit can also refer to the user's past nutritional intake history and predict the intake amount of a specific nutrient to provide optimal information. As a result, optimal nutrient information can be provided by referring to the user's past nutritional intake history.

[0051] The providing unit can provide optimal information in consideration of the geographical location information of the user when providing nutrient information. The providing unit uses AI to provide optimal information in consideration of the geographical location information of the user when providing nutrient information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the providing unit can preferentially provide information on nutrients related to highland. In addition, when the user is in an urban area, the providing unit can also preferentially provide information on nutrients related to urban areas. Furthermore, when the user is on the seashore, the providing unit can also preferentially provide information on nutrients related to the seashore. This allows more appropriate information to be provided by providing optimal nutrient information in consideration of the geographical location information of the user.

[0052] The linking unit can select an appropriate linking method based on the user's past meal history when linking menu information. The linking unit uses AI to select the optimal linking method by referring to the user's past meal history when linking menu information. The past meal history includes, for example, past meal contents, records of ingested nutrients, etc., but is not limited to such examples. The linking unit links current menu information based on menus selected by the user in the past. In addition, the linking unit can also analyze the selection frequency of a specific menu from the user's past meal history and select the optimal linking method. Furthermore, the linking unit can also refer to the user's past meal history and predict the selection tendency of a specific menu to select the optimal linking method. In this way, the optimal menu information linking method can be selected by referring to the user's past meal history.

[0053] The linking unit can select an optimal linking method in consideration of the geographical location information of the user when linking the menu information. The linking unit uses AI to select an optimal linking method in consideration of the geographical location information of the user when linking the menu information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is at high altitude, the linking unit preferentially links menu information related to high altitude. In addition, when the user is in an urban area, the linking unit can also preferentially link menu information related to urban areas. Furthermore, when the user is at the seaside, the linking unit can also preferentially link menu information related to the seaside. As a result, by selecting an optimal linking method of menu information in consideration of the geographical location information of the user, it is possible to provide more appropriate menu information.

[0054] The recipe unit can provide an optimal recipe by referring to the user's past cooking history when suggesting a recipe. The recipe unit uses AI to provide an optimal recipe by referring to the user's past cooking history when suggesting a recipe. The past cooking history includes, for example, dishes made in the past, ingredients used, etc., but is not limited to such examples. The recipe unit, for example, suggests a current recipe based on dishes made by the user in the past. The recipe unit can also analyze the frequency of making a specific dish from the user's past cooking history and provide an optimal recipe. Furthermore, the recipe unit can also refer to the user's past cooking history and predict the tendency to make a specific dish to provide an optimal recipe. In this way, the optimal recipe can be provided by referring to the user's past cooking history.

[0055] The recipe unit can provide an optimal recipe in consideration of the geographical location information of the user when proposing a recipe. The recipe unit uses AI to provide an optimal recipe in consideration of the geographical location information of the user when proposing a recipe. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the recipe unit can preferentially provide recipes related to highland. In addition, when the user is in an urban area, the recipe unit can also preferentially provide recipes related to urban areas. Furthermore, when the user is on the seashore, the recipe unit can also preferentially provide recipes related to the seashore. This makes it possible to provide a more appropriate recipe by providing an optimal recipe in consideration of the geographical location information of the user.

[0056] The identification unit can improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The identification unit identifies nutrients that are currently lacking based on nutrients taken by the user in the past. The identification unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history to improve the accuracy of identification. Furthermore, the identification unit can also improve the accuracy of identification by referring to the user's past nutritional intake history and predicting the intake amount of a specific nutrient. Thereby, the accuracy of identification can be improved by referring to the user's past nutritional intake history.

[0057] The identification unit can customize the specific means based on the user's current living situation when identifying nutrients. The identification unit uses AI to customize the specific means based on the user's current living situation when identifying nutrients. The current living situation includes, but is not limited to, for example, work situation, home environment, and lifestyle. For example, when the user is at work, the identification unit preferentially identifies nutrients related to work. In addition, when the user is on vacation, the identification unit can also preferentially identify nutrients related to relaxation. Furthermore, when the user is exercising, the identification unit can also preferentially identify nutrients related to exercise. This makes it possible to identify more appropriate nutrients by customizing the specific means based on the user's current living situation.

[0058] The identification unit can identify optimal nutrients by considering the geographical location information of the user when identifying nutrients. The identification unit uses AI to identify optimal nutrients by considering the geographical location information of the user when identifying nutrients. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the identification unit preferentially identifies nutrients related to highland. In addition, when the user is in an urban area, the identification unit can also preferentially identify nutrients related to urban areas. Furthermore, when the user is on the seashore, the identification unit can also preferentially identify nutrients related to the seashore. In this way, it is possible to identify more appropriate nutrients by identifying optimal nutrients by considering the geographical location information of the user.

[0059] The identification unit can improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, when a user posts about fatigue on social media, the identification unit preferentially identifies nutrients related to fatigue. In addition, when a user posts about stress on social media, the identification unit can also preferentially identify nutrients related to stress. Furthermore, when a user posts about health on social media, the identification unit can also preferentially identify nutrients related to health. This makes it possible to improve the accuracy of identification by analyzing the user's social media activity.

[0060] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the nutrient using AI at the time of suggestion. The importance of a nutrient includes, for example, the degree of impact on health and the necessity of intake, but is not limited to such examples. For example, the suggestion unit makes a proposal including detailed information regarding important nutrients. In addition, the suggestion unit can also make a proposal including concise information regarding nutrients that are not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the nutrient. As a result, a more appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the nutrient.

[0061] The suggestion unit can apply different suggestion algorithms depending on the nutrient category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the nutrient category at the time of suggestion. Nutrient categories include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. For example, the suggestion unit applies an algorithm that takes into account the absorption rate of vitamins to a suggestion regarding vitamins. In addition, the suggestion unit can also apply an algorithm that takes into account the interaction of minerals to a suggestion regarding minerals. Furthermore, the suggestion unit can apply an algorithm that takes into account the digestion and absorption of proteins to a proposal regarding proteins. In this way, more appropriate suggestions can be made by applying the optimal suggestion algorithm according to the nutrient category.

[0062] The suggestion unit can determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The time when the nutrient was specified includes, but is not limited to, the season, the health condition of the user, and the like. For example, if the time when the nutrient was specified is recent, the suggestion unit can preferentially suggest the nutrient. In addition, if the time when the nutrient was specified is in the past, the suggestion unit can also lower the priority of the nutrient. Furthermore, the suggestion unit can gradually adjust the priority of the suggestion based on the time when the nutrient was specified. In this way, more appropriate suggestions can be made by determining the priority of the suggestion based on the time when the nutrient was specified.

[0063] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. The suggestion unit adjusts the order of suggestions based on the relevance of nutrients using AI at the time of suggestion. Nutrient relevance includes, for example, interactions and synergistic effects, but is not limited to such examples. For example, the suggestion unit preferentially suggests nutrients with high relevance. In addition, the suggestion unit can postpone the suggestion order of nutrients with low relevance. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of nutrients. Thereby, by adjusting the order of suggestions based on the relevance of nutrients, more appropriate suggestions can be made.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can also analyze the user's lifestyle habits and make suggestions for improving the lifestyle habits based on the input subjective symptoms. For example, if the user inputs "I get tired easily," the reception unit analyzes the user's sleep patterns and exercise habits and suggests appropriate sleep times and exercise. If the user inputs "I tend to get stiff shoulders," the reception unit can also suggest desk work posture and stretching. Furthermore, if the user inputs "I often get headaches," the reception unit can also suggest fluid intake and stress management. This allows the user to receive specific advice for improving their lifestyle habits based on their subjective symptoms.

[0066] The providing unit can also customize the nutritional information of the suggested meal by taking into account the user's allergy information. For example, if the user has a nut allergy, the providing unit can suggest nut-free substitute ingredients. Also, if the user has a dairy allergy, the providing unit can suggest recipes that do not contain dairy products. Furthermore, if the user has a gluten allergy, the providing unit can suggest gluten-free meals. This allows the user to choose safe meals that take allergies into consideration.

[0067] The linking unit can also learn the user's food preferences and tastes and customize the menu information it proposes. For example, if the user likes Japanese food, the linking unit will preferentially propose Japanese food menus. Also, if the user is a vegetarian, the linking unit can also propose vegetarian menus. Furthermore, if the user likes a particular ingredient, the linking unit can also propose menus that include that ingredient. This allows the user to choose meals that suit their preferences.

[0068] The recipe unit can also adjust the difficulty of the suggested recipes taking into account the cooking skill level of the user. For example, if the user is a beginner, the recipe unit can suggest simple and basic recipes. If the user is an intermediate cook, the recipe unit can suggest slightly more complicated recipes. Furthermore, if the user is an advanced cook, the recipe unit can suggest recipes that require advanced techniques. This allows the user to choose recipes that suit their cooking skills.

[0069] The identification unit can also identify nutrients taking into account the genetic information of the user. For example, if the user has genetic poor absorption of vitamin D, the identification unit can suggest a diet rich in vitamin D. Also, if the user has genetic poor absorption of iron, the identification unit can also suggest a diet rich in iron. Furthermore, if the user has genetic slow metabolism of a particular nutrient, the identification unit can also suggest a diet for properly ingesting that nutrient. This allows the user to ingest optimal nutrients based on their genetic information.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The reception unit inputs the subjective symptoms of the user. The subjective symptoms of the user include, but are not limited to, for example, headache, fatigue, stomach discomfort, etc. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. Step 2: The identification unit uses AI to identify nutrients based on the subjective symptoms input by the reception unit. Examples of nutrients include, but are not limited to, vitamins, minerals, and proteins. For example, the identification unit uses AI to identify nutrients based on past data and medical knowledge. The identification unit can also identify nutrients by using AI to refer to the user's past diet history and health checkup results. Step 3: The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. Meals include, but are not limited to, breakfast, lunch, dinner, and snacks. For example, when the user eats at a convenience store or a chain restaurant, the suggestion unit suggests recommended menus in cooperation with menu information. In addition, the suggestion unit can also suggest recipes when the user selects to cook at home.

[0072] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that identifies nutrients that may be lacking in the body based on the discomfort or subjective symptoms felt by the user and suggests appropriate meals. In this AI system, the user inputs subjective symptoms, and the AI ​​analyzes the information to identify the nutrients that are lacking and suggests meals. For example, if the user inputs symptoms such as "easily tired," "easily stiff shoulders," and "frequent headaches," the AI ​​identifies that there is a possibility of a deficiency in iron or B vitamins based on the information. Next, the AI ​​suggests meals to supplement the identified nutrients. The suggestions vary depending on where the user eats. For example, if the user eats at a convenience store or chain restaurant, the AI ​​suggests recommended menus in cooperation with menu information. In addition, if the user selects to cook at home, the AI ​​suggests recipes. In addition, the AI ​​provides nutrient information for the suggested meals. For example, details of the nutrients contained in the proposed menus and recipes are displayed so that the user can check which nutrients can be ingested. This allows the user to choose an appropriate meal that matches their physical condition. For example, a user who feels easily tired can reduce fatigue by following the AI's suggestions and eating meals rich in iron and B vitamins. Additionally, users who tend to suffer from stiff shoulders can reduce their stiffness by following the AI's suggestions and eating meals rich in magnesium and vitamin E. This allows the AI ​​system to identify appropriate nutrients and suggest meals based on the user's subjective symptoms.

[0073] The AI ​​system according to the embodiment includes a reception unit, an identification unit, and a suggestion unit. The reception unit inputs a subjective symptom of a user. The subjective symptoms of the user include, for example, headache, fatigue, stomach discomfort, etc., but are not limited to such examples. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. The identification unit uses AI to identify nutrients based on the subjective symptoms input by the reception unit. The nutrients include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. The identification unit identifies nutrients based on, for example, past data and medical knowledge by the AI. The identification unit can also identify nutrients by referring to the user's past diet history and health checkup results by the AI. The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. The meals include, for example, breakfast, lunch, dinner, snacks, etc., but are not limited to such examples. For example, when the user eats at a convenience store or a chain restaurant, the suggestion unit suggests a recommended menu in cooperation with menu information. In addition, the suggestion unit can also suggest recipes when the user selects home cooking. This allows the AI ​​system according to the embodiment to identify appropriate nutrients based on the user's subjective symptoms and suggest meals.

[0074] The reception unit inputs the subjective symptoms of the user. The subjective symptoms of the user include, for example, headache, fatigue, stomach discomfort, etc., but are not limited to such examples. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. Specifically, the user can input the subjective symptoms in text format through a dedicated application or web interface of the smartphone or personal computer. In the case of voice input, the user's utterance is converted into text data using voice recognition technology and imported into the system. In the case of using a touch panel, the user can easily input the subjective symptoms by tapping on an option on the screen. Furthermore, the reception unit can temporarily store the user's input data and refer to the past input history as necessary. This makes it possible for the user to recheck the subjective symptoms entered in the past or to quickly respond if a similar symptom recurs. In addition, the reception unit encrypts and stores the user's input data, and takes security measures to protect privacy. This allows the user to input the subjective symptoms with peace of mind.

[0075] The identification unit uses AI to identify nutrients based on subjective symptoms input by the reception unit. Examples of nutrients include, but are not limited to, vitamins, minerals, and proteins. For example, the identification unit uses AI to identify nutrients based on past data and medical knowledge. In addition, the identification unit can also identify nutrients by referring to the user's past dietary history and health checkup results. Specifically, the AI ​​uses natural language processing technology to analyze the subjective symptoms input by the user and identify related nutrients. For example, if a headache symptom is input, the AI ​​considers the possibility that it may be caused by a lack of vitamin B2 or magnesium. In addition, if a feeling of fatigue is input, a lack of iron or vitamin B12 may be considered. Furthermore, the AI ​​refers to a large-scale medical database and research papers to identify nutrients based on the latest medical knowledge. When referring to the user's past dietary history and health checkup results, the AI ​​performs an integrated analysis of these data and comprehensively evaluates the user's nutritional state. For example, if it is found from the past dietary history that a specific nutrient is lacking, a suggestion is made to supplement that nutrient. In addition, if the health checkup results indicate an excessive intake of a particular nutrient, the identification unit will suggest that the user refrain from taking in that nutrient. This allows the identification unit to identify optimal nutrients according to the individual situation of the user, thereby contributing to the maintenance and improvement of health.

[0076] The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. Meals include, for example, breakfast, lunch, dinner, and snacks, but are not limited to such examples. For example, when the user eats at a convenience store or chain restaurant, the suggestion unit suggests recommended menus in cooperation with menu information. In addition, when the user selects home cooking, the suggestion unit can also suggest recipes. Specifically, the AI ​​suggests optimal meal menus based on the identified nutrients, taking into consideration the user's preferences and allergy information. For example, if the user is deficient in vitamin C, the AI ​​suggests menus using fruits and vegetables that are rich in vitamin C. When the user eats at a convenience store or chain restaurant, the AI ​​obtains the store's menu information in real time and selects menus with balanced nutrients. For example, a specific salad or sandwich may be suggested. When the user selects home cooking, the AI ​​suggests specific recipes and provides detailed explanations of the necessary ingredients and cooking procedures. In addition, the suggestion unit records the user's meal history and improves the accuracy of the suggestions based on past suggestions and user feedback. For example, if the user likes a menu that was suggested in the past, a similar menu can be suggested again. The suggestion unit also analyzes the user's diet history, detects imbalances in nutritional balance, and makes suggestions to adjust dietary content as necessary, thereby making it possible for the suggestion unit to make specific dietary suggestions to maintain and improve the user's health condition.

[0077] The system includes a providing unit that provides nutrient information of the proposed meal. The providing unit provides the nutrient information of the proposed meal to the user. The nutrient information includes, for example, nutrient content, health effects, and the like, but is not limited to such examples. The providing unit, for example, displays details of nutrients contained in a proposed menu or recipe, allowing the user to confirm which nutrients can be ingested. The providing unit can also display the nutrient information graphically. For example, the nutrient content is displayed in a graph or chart, and provided in a visually easy-to-understand format. This makes it possible to provide the nutrient information of the proposed meal to the user.

[0078] The device includes a linking unit that links with menu information of convenience stores or chain stores. The linking unit links with menu information of convenience stores or chain stores. Menu information includes, for example, dish names, nutritional components, prices, etc., but is not limited to these examples. The linking unit, for example, obtains menu information of convenience stores or chain stores in real time and provides it to the user. The linking unit can also analyze the menu information using AI and suggest the most suitable menu to the user. This makes it possible to suggest appropriate menus in collaboration with menu information of convenience stores or chain stores.

[0079] The device includes a recipe unit that suggests recipes when home cooking is selected. The recipe unit suggests recipes when home cooking is selected. Recipes include, but are not limited to, ingredients, cooking steps, and cooking times. For example, when a user selects home cooking, the recipe unit uses AI to suggest an appropriate recipe. The recipe unit can also provide nutritional information for the suggested recipe. This makes it possible to suggest appropriate recipes when home cooking is selected.

[0080] The identification unit can identify nutrients based on past data or medical knowledge. The identification unit uses AI to identify nutrients based on past data or medical knowledge. Past data includes, for example, past dietary history, health checkup results, etc., but is not limited to such examples. Medical knowledge includes, for example, nutritional knowledge, and the relationship between symptoms of illness and nutrients, but is not limited to such examples. For example, the identification unit uses AI to analyze past dietary history and identify nutrients that are currently lacking. The identification unit can also identify nutrients related to the user's subjective symptoms based on AI medical knowledge. This makes it possible to make highly accurate suggestions by identifying nutrients based on past data and medical knowledge.

[0081] The suggestion unit can change the suggestion content depending on where the user eats. The suggestion unit uses AI to change the suggestion content depending on where the user eats. The eating place includes, but is not limited to, for example, a home, a restaurant, and a workplace. For example, when the user eats at home, the suggestion unit makes a recipe suggestion. In addition, when the user eats at a restaurant, the suggestion unit can also suggest a recommended menu in cooperation with menu information. This allows more appropriate suggestions to be made by changing the suggestion content depending on where the user eats.

[0082] The reception unit can estimate the user's emotion and adjust the input timing of the subjective symptoms based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and adjust the input timing of the subjective symptoms based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is feeling stressed, the reception unit prompts the user to input the subjective symptoms during a time period when the user can relax. In addition, when the user is relaxed, the reception unit can also prompt the user to input the subjective symptoms immediately. Furthermore, when the user is busy, the reception unit can also prompt the user to input the subjective symptoms during a time period when the user is not busy. In this way, by adjusting the input timing of the subjective symptoms according to the user's emotion, it is possible to prompt the user to input at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The reception unit can analyze the user's past subjective symptom history and select an appropriate input method. The reception unit uses AI to analyze the user's past subjective symptom history and select an appropriate input method. The past subjective symptom history includes, for example, past symptom records, frequency, duration, etc., but is not limited to such examples. The reception unit, for example, automatically displays subjective symptoms that the user frequently inputs in the past as candidates. In addition, the reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest subjective symptoms to be input in a specific time period from the user's past subjective symptom history. In this way, the optimal input method can be selected by analyzing the user's past subjective symptom history.

[0084] The reception unit can perform filtering based on the user's current living situation or field of interest when inputting subjective symptoms. The reception unit uses AI to perform filtering based on the user's current living situation or field of interest when inputting subjective symptoms. The current living situation includes, for example, work situation, home environment, lifestyle, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, health interests, etc., but is not limited to such examples. For example, when the user is at work, the reception unit preferentially inputs subjective symptoms related to work. In addition, when the user is on vacation, the reception unit can also preferentially input subjective symptoms related to relaxation. Furthermore, when the user is exercising, the reception unit can also preferentially input subjective symptoms related to exercise. In this way, by filtering based on the user's current living situation and field of interest, it is possible to encourage the input of more appropriate subjective symptoms.

[0085] The reception unit can estimate the user's emotion and determine the priority order of subjective symptoms to be input based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and determine the priority order of subjective symptoms to be input based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the reception unit preferentially inputs subjective symptoms related to stress. In addition, when the user is relaxed, the reception unit can also preferentially input subjective symptoms related to relaxation. Furthermore, when the user is tired, the reception unit can also preferentially input subjective symptoms related to fatigue. In this way, by determining the priority order of subjective symptoms according to the user's emotion, it is possible to encourage the input of more appropriate subjective symptoms. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The reception unit can preferentially input highly relevant symptoms based on the geographical location information of the user when inputting subjective symptoms. The reception unit uses AI to preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting subjective symptoms. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is at high altitude, the reception unit preferentially inputs subjective symptoms related to high altitude. In addition, when the user is in an urban area, the reception unit can also preferentially input subjective symptoms related to urban areas. Furthermore, when the user is at the seaside, the reception unit can also preferentially input subjective symptoms related to the seaside. As a result, it is possible to encourage the input of more appropriate subjective symptoms by preferentially inputting highly relevant symptoms in consideration of the geographical location information of the user.

[0087] The reception unit can analyze the user's social media activity and input related symptoms when the subjective symptoms are input. The reception unit uses AI to analyze the user's social media activity and input related symptoms when the subjective symptoms are input. Social media activity includes, for example, the content of the post, the number of likes, comments, and the like, but is not limited to such examples. For example, when the user posts about fatigue on social media, the reception unit preferentially inputs subjective symptoms related to fatigue. In addition, when the user posts about stress on social media, the reception unit can also preferentially input subjective symptoms related to stress. Furthermore, when the user posts about health on social media, the reception unit can also preferentially input subjective symptoms related to health. In this way, the input of related subjective symptoms can be prompted by analyzing the user's social media activity.

[0088] The identification unit can estimate the user's emotion and adjust the nutrient identification method based on the estimated user's emotion. The identification unit uses AI to estimate the user's emotion and adjust the nutrient identification method based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the identification unit preferentially identifies nutrients that are useful for reducing stress. In addition, when the user is relaxed, the identification unit can also preferentially identify nutrients for maintaining relaxation. Furthermore, when the user is tired, the identification unit can also preferentially identify nutrients that are useful for recovering from fatigue. In this way, by adjusting the nutrient identification method according to the user's emotion, more appropriate nutrients can be identified. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The identification unit can improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The identification unit identifies nutrients that are currently lacking based on nutrients taken by the user in the past. The identification unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history to improve the accuracy of identification. Furthermore, the identification unit can also improve the accuracy of identification by referring to the user's past nutritional intake history and predicting the intake amount of a specific nutrient. Thereby, the accuracy of identification can be improved by referring to the user's past nutritional intake history.

[0090] The identification unit can customize the specific means based on the user's current living situation when identifying nutrients. The identification unit uses AI to customize the specific means based on the user's current living situation when identifying nutrients. The current living situation includes, but is not limited to, for example, work situation, home environment, and lifestyle. For example, when the user is at work, the identification unit preferentially identifies nutrients related to work. In addition, when the user is on vacation, the identification unit can also preferentially identify nutrients related to relaxation. Furthermore, when the user is exercising, the identification unit can also preferentially identify nutrients related to exercise. This makes it possible to identify more appropriate nutrients by customizing the specific means based on the user's current living situation.

[0091] The identification unit can estimate the user's emotion and determine the priority order of nutrients to be identified based on the estimated user's emotion. The identification unit uses AI to estimate the user's emotion and determine the priority order of nutrients to be identified based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the identification unit preferentially identifies nutrients that are useful for reducing stress. In addition, when the user is relaxed, the identification unit can also preferentially identify nutrients for maintaining relaxation. Furthermore, when the user is tired, the identification unit can also preferentially identify nutrients that are useful for recovering from fatigue. In this way, by determining the priority order of nutrients according to the user's emotion, more appropriate nutrients can be identified. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The identification unit can identify appropriate nutrients based on the geographical location information of the user when identifying nutrients. The identification unit uses AI to identify optimal nutrients taking into account the geographical location information of the user when identifying nutrients. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. For example, when the user is in a highland, the identification unit preferentially identifies nutrients related to highland. In addition, when the user is in an urban area, the identification unit can also preferentially identify nutrients related to urban areas. Furthermore, when the user is on the seashore, the identification unit can also preferentially identify nutrients related to the seashore. This allows more appropriate nutrients to be identified by identifying optimal nutrients taking into account the geographical location information of the user.

[0093] The identification unit can improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, when a user posts about fatigue on social media, the identification unit preferentially identifies nutrients related to fatigue. In addition, when a user posts about stress on social media, the identification unit can also preferentially identify nutrients related to stress. Furthermore, when a user posts about health on social media, the identification unit can also preferentially identify nutrients related to health. This makes it possible to improve the accuracy of identification by analyzing the user's social media activity.

[0094] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the suggestion unit makes a proposal in an expression method that allows the user to relax. In addition, when the user is relaxed, the suggestion unit can also make a proposal in an expression method that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a proposal in an expression method that is concise and to the point. In this way, by adjusting the expression method of the proposal according to the user's emotion, a more appropriate proposal can be made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the nutrient using AI at the time of suggestion. The importance of a nutrient includes, for example, the degree of impact on health and the necessity of intake, but is not limited to such examples. For example, the suggestion unit makes a proposal including detailed information regarding important nutrients. In addition, the suggestion unit can also make a proposal including concise information regarding nutrients that are not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the nutrient. As a result, a more appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the nutrient.

[0096] The suggestion unit can apply different suggestion algorithms depending on the nutrient category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the nutrient category at the time of suggestion. Nutrient categories include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. For example, the suggestion unit applies an algorithm that takes into account the absorption rate of vitamins to a suggestion regarding vitamins. In addition, the suggestion unit can also apply an algorithm that takes into account the interaction of minerals to a suggestion regarding minerals. Furthermore, the suggestion unit can apply an algorithm that takes into account the digestion and absorption of proteins to a proposal regarding proteins. In this way, more appropriate suggestions can be made by applying the optimal suggestion algorithm according to the nutrient category.

[0097] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the suggestion unit makes a short and to the point proposal. In addition, when the user is relaxed, the suggestion unit can also make a longer proposal including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a quick and concise proposal. In this way, by adjusting the length of the proposal according to the user's emotion, more appropriate proposals can be made. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] The suggestion unit can determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The time when the nutrient was specified includes, but is not limited to, the season, the health condition of the user, and the like. For example, if the time when the nutrient was specified is recent, the suggestion unit can preferentially suggest the nutrient. In addition, if the time when the nutrient was specified is in the past, the suggestion unit can also lower the priority of the nutrient. Furthermore, the suggestion unit can gradually adjust the priority of the suggestion based on the time when the nutrient was specified. In this way, more appropriate suggestions can be made by determining the priority of the suggestion based on the time when the nutrient was specified.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. The suggestion unit adjusts the order of suggestions based on the relevance of nutrients using AI at the time of suggestion. Nutrient relevance includes, for example, interactions and synergistic effects, but is not limited to such examples. For example, the suggestion unit preferentially suggests nutrients with high relevance. In addition, the suggestion unit can postpone the suggestion order of nutrients with low relevance. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of nutrients. Thereby, by adjusting the order of suggestions based on the relevance of nutrients, more appropriate suggestions can be made.

[0100] The providing unit can estimate the user's emotion and adjust the display method of the nutrient information based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the display method of the nutrient information based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the providing unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the nutrient information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The providing unit can provide appropriate information based on the user's past nutritional intake history when providing nutrient information. The providing unit uses AI to provide optimal information by referring to the user's past nutritional intake history when providing nutrient information. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The providing unit provides information on nutrients currently lacking based on nutrients taken by the user in the past, for example. The providing unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history and provide optimal information. Furthermore, the providing unit can also refer to the user's past nutritional intake history and predict the intake amount of a specific nutrient to provide optimal information. As a result, optimal nutrient information can be provided by referring to the user's past nutritional intake history.

[0102] The providing unit can estimate the user's emotion and determine the priority of the nutrient information based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and determine the priority of the nutrient information based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the providing unit can preferentially provide information on nutrients that are useful for reducing stress. In addition, when the user is relaxed, the providing unit can also preferentially provide information on nutrients for maintaining relaxation. Furthermore, when the user is tired, the providing unit can also preferentially provide information on nutrients that are useful for recovering from fatigue. This makes it possible to provide more appropriate information by determining the priority of nutrient information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The providing unit can provide optimal information in consideration of the geographical location information of the user when providing nutrient information. The providing unit uses AI to provide optimal information in consideration of the geographical location information of the user when providing nutrient information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the providing unit can preferentially provide information on nutrients related to highland. In addition, when the user is in an urban area, the providing unit can also preferentially provide information on nutrients related to urban areas. Furthermore, when the user is on the seashore, the providing unit can also preferentially provide information on nutrients related to the seashore. This allows more appropriate information to be provided by providing optimal nutrient information in consideration of the geographical location information of the user.

[0104] The linking unit can estimate the user's emotion and adjust the linking method of the menu information based on the estimated user's emotion. The linking unit uses AI to estimate the user's emotion and adjusts the linking method of the menu information based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the linking unit preferentially links menu information that is useful for reducing stress. In addition, when the user is relaxed, the linking unit can also preferentially link menu information for maintaining relaxation. Furthermore, when the user is tired, the linking unit can also preferentially link menu information that is useful for recovering from fatigue. This makes it possible to provide more appropriate menu information by adjusting the linking method of the menu information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] The linking unit can select an appropriate linking method based on the user's past meal history when linking menu information. The linking unit uses AI to select the optimal linking method by referring to the user's past meal history when linking menu information. The past meal history includes, for example, past meal contents, records of ingested nutrients, etc., but is not limited to such examples. The linking unit links current menu information based on menus selected by the user in the past. In addition, the linking unit can also analyze the selection frequency of a specific menu from the user's past meal history and select the optimal linking method. Furthermore, the linking unit can also refer to the user's past meal history and predict the selection tendency of a specific menu to select the optimal linking method. In this way, the optimal menu information linking method can be selected by referring to the user's past meal history.

[0106] The linking unit can estimate the user's emotion and determine the priority order of the menu information based on the estimated user's emotion. The linking unit uses AI to estimate the user's emotion and determine the priority order of the menu information based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the linking unit can preferentially provide menu information that helps reduce stress. In addition, when the user is relaxed, the linking unit can also preferentially provide menu information for maintaining relaxation. Furthermore, when the user is tired, the linking unit can also preferentially provide menu information that helps recover from fatigue. This makes it possible to provide more appropriate menu information by determining the priority order of the menu information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The linking unit can select an optimal linking method in consideration of the geographical location information of the user when linking the menu information. The linking unit uses AI to select an optimal linking method in consideration of the geographical location information of the user when linking the menu information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is at high altitude, the linking unit preferentially links menu information related to high altitude. In addition, when the user is in an urban area, the linking unit can also preferentially link menu information related to urban areas. Furthermore, when the user is at the seaside, the linking unit can also preferentially link menu information related to the seaside. As a result, by selecting an optimal linking method of menu information in consideration of the geographical location information of the user, it is possible to provide more appropriate menu information.

[0108] The recipe unit can estimate the user's emotion and adjust the recipe suggestion method based on the estimated user's emotion. The recipe unit uses AI to estimate the user's emotion and adjust the recipe suggestion method based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the recipe unit preferentially suggests recipes that are useful for reducing stress. In addition, when the user is relaxed, the recipe unit can also preferentially suggest recipes for maintaining relaxation. Furthermore, when the user is tired, the recipe unit can also preferentially suggest recipes that are useful for recovering from fatigue. This makes it possible to provide more appropriate recipes by adjusting the recipe suggestion method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] The recipe unit can provide an optimal recipe by referring to the user's past cooking history when suggesting a recipe. The recipe unit uses AI to provide an optimal recipe by referring to the user's past cooking history when suggesting a recipe. The past cooking history includes, for example, dishes made in the past, ingredients used, etc., but is not limited to such examples. The recipe unit, for example, suggests a current recipe based on dishes made by the user in the past. The recipe unit can also analyze the frequency of making a specific dish from the user's past cooking history and provide an optimal recipe. Furthermore, the recipe unit can also refer to the user's past cooking history and predict the tendency to make a specific dish to provide an optimal recipe. In this way, the optimal recipe can be provided by referring to the user's past cooking history.

[0110] The recipe unit can estimate the user's emotion and determine the priority order of recipes based on the estimated user's emotion. The recipe unit uses AI to estimate the user's emotion and determine the priority order of recipes based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is feeling stressed, the recipe unit can preferentially provide recipes that are useful for reducing stress. In addition, when the user is relaxed, the recipe unit can also preferentially provide recipes for maintaining relaxation. Furthermore, when the user is tired, the recipe unit can also preferentially provide recipes that are useful for recovering from fatigue. This makes it possible to provide more appropriate recipes by determining the priority order of recipes according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The recipe unit can provide an optimal recipe in consideration of the geographical location information of the user when proposing a recipe. The recipe unit uses AI to provide an optimal recipe in consideration of the geographical location information of the user when proposing a recipe. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the recipe unit can preferentially provide recipes related to highland. In addition, when the user is in an urban area, the recipe unit can also preferentially provide recipes related to urban areas. Furthermore, when the user is on the seashore, the recipe unit can also preferentially provide recipes related to the seashore. This makes it possible to provide a more appropriate recipe by providing an optimal recipe in consideration of the geographical location information of the user.

[0112] The identification unit can estimate the user's emotion and adjust the nutrient identification method based on the estimated user's emotion. The identification unit uses AI to estimate the user's emotion and adjust the nutrient identification method based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the identification unit preferentially identifies nutrients that are useful for reducing stress. In addition, when the user is relaxed, the identification unit can also preferentially identify nutrients for maintaining relaxation. Furthermore, when the user is tired, the identification unit can also preferentially identify nutrients that are useful for recovering from fatigue. In this way, by adjusting the nutrient identification method according to the user's emotion, more appropriate nutrients can be identified. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The identification unit can improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by referring to the user's past nutritional intake history when identifying nutrients. The past nutritional intake history includes, for example, past meal contents, records of nutrients taken, etc., but is not limited to such examples. The identification unit identifies nutrients that are currently lacking based on nutrients taken by the user in the past. The identification unit can also analyze the intake frequency of a specific nutrient from the user's past nutritional intake history to improve the accuracy of identification. Furthermore, the identification unit can also improve the accuracy of identification by referring to the user's past nutritional intake history and predicting the intake amount of a specific nutrient. Thereby, the accuracy of identification can be improved by referring to the user's past nutritional intake history.

[0114] The identification unit can customize the specific means based on the user's current living situation when identifying nutrients. The identification unit uses AI to customize the specific means based on the user's current living situation when identifying nutrients. The current living situation includes, but is not limited to, for example, work situation, home environment, and lifestyle. For example, when the user is at work, the identification unit preferentially identifies nutrients related to work. In addition, when the user is on vacation, the identification unit can also preferentially identify nutrients related to relaxation. Furthermore, when the user is exercising, the identification unit can also preferentially identify nutrients related to exercise. This makes it possible to identify more appropriate nutrients by customizing the specific means based on the user's current living situation.

[0115] The identification unit can estimate the user's emotion and determine the priority order of nutrients to be identified based on the estimated user's emotion. The identification unit uses AI to estimate the user's emotion and determine the priority order of nutrients to be identified based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the identification unit preferentially identifies nutrients that are useful for reducing stress. In addition, when the user is relaxed, the identification unit can also preferentially identify nutrients for maintaining relaxation. Furthermore, when the user is tired, the identification unit can also preferentially identify nutrients that are useful for recovering from fatigue. In this way, by determining the priority order of nutrients according to the user's emotion, more appropriate nutrients can be identified. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0116] The identification unit can identify optimal nutrients by considering the geographical location information of the user when identifying nutrients. The identification unit uses AI to identify optimal nutrients by considering the geographical location information of the user when identifying nutrients. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a highland, the identification unit preferentially identifies nutrients related to highland. In addition, when the user is in an urban area, the identification unit can also preferentially identify nutrients related to urban areas. Furthermore, when the user is on the seashore, the identification unit can also preferentially identify nutrients related to the seashore. In this way, it is possible to identify more appropriate nutrients by identifying optimal nutrients by considering the geographical location information of the user.

[0117] The identification unit can improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. The identification unit uses AI to improve the accuracy of identification by analyzing the user's social media activity when identifying nutrients. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, when a user posts about fatigue on social media, the identification unit preferentially identifies nutrients related to fatigue. In addition, when a user posts about stress on social media, the identification unit can also preferentially identify nutrients related to stress. Furthermore, when a user posts about health on social media, the identification unit can also preferentially identify nutrients related to health. This makes it possible to improve the accuracy of identification by analyzing the user's social media activity.

[0118] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the suggestion unit makes a proposal in an expression method that allows the user to relax. In addition, when the user is relaxed, the suggestion unit can also make a proposal in an expression method that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a proposal in an expression method that is concise and to the point. In this way, by adjusting the expression method of the proposal according to the user's emotion, a more appropriate proposal can be made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the nutrient using AI at the time of suggestion. The importance of a nutrient includes, for example, the degree of impact on health and the necessity of intake, but is not limited to such examples. For example, the suggestion unit makes a proposal including detailed information regarding important nutrients. In addition, the suggestion unit can also make a proposal including concise information regarding nutrients that are not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the nutrient. As a result, a more appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the nutrient.

[0120] The suggestion unit can apply different suggestion algorithms depending on the nutrient category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the nutrient category at the time of suggestion. Nutrient categories include, for example, vitamins, minerals, proteins, etc., but are not limited to such examples. For example, the suggestion unit applies an algorithm that takes into account the absorption rate of vitamins to a suggestion regarding vitamins. In addition, the suggestion unit can also apply an algorithm that takes into account the interaction of minerals to a suggestion regarding minerals. Furthermore, the suggestion unit can apply an algorithm that takes into account the digestion and absorption of proteins to a proposal regarding proteins. In this way, more appropriate suggestions can be made by applying the optimal suggestion algorithm according to the nutrient category.

[0121] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the suggestion unit makes a short and to the point proposal. In addition, when the user is relaxed, the suggestion unit can also make a longer proposal including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a quick and concise proposal. In this way, by adjusting the length of the proposal according to the user's emotion, more appropriate proposals can be made. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] The suggestion unit can determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestion based on the time when the nutrient was specified at the time of suggestion. The time when the nutrient was specified includes, but is not limited to, the season, the health condition of the user, and the like. For example, if the time when the nutrient was specified is recent, the suggestion unit can preferentially suggest the nutrient. In addition, if the time when the nutrient was specified is in the past, the suggestion unit can also lower the priority of the nutrient. Furthermore, the suggestion unit can gradually adjust the priority of the suggestion based on the time when the nutrient was specified. In this way, more appropriate suggestions can be made by determining the priority of the suggestion based on the time when the nutrient was specified.

[0123] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. The suggestion unit adjusts the order of suggestions based on the relevance of nutrients using AI at the time of suggestion. Nutrient relevance includes, for example, interactions and synergistic effects, but is not limited to such examples. For example, the suggestion unit preferentially suggests nutrients with high relevance. In addition, the suggestion unit can postpone the suggestion order of nutrients with low relevance. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of nutrients. Thereby, by adjusting the order of suggestions based on the relevance of nutrients, more appropriate suggestions can be made.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The reception unit can also analyze the user's lifestyle habits and make suggestions for improving the lifestyle habits based on the input subjective symptoms. For example, if the user inputs "I get tired easily," the reception unit analyzes the user's sleep patterns and exercise habits and suggests appropriate sleep times and exercise. If the user inputs "I tend to get stiff shoulders," the reception unit can also suggest desk work posture and stretching. Furthermore, if the user inputs "I often get headaches," the reception unit can also suggest fluid intake and stress management. This allows the user to receive specific advice for improving their lifestyle habits based on their subjective symptoms.

[0126] The providing unit can also customize the nutritional information of the suggested meal by taking into account the user's allergy information. For example, if the user has a nut allergy, the providing unit can suggest nut-free substitute ingredients. Also, if the user has a dairy allergy, the providing unit can suggest recipes that do not contain dairy products. Furthermore, if the user has a gluten allergy, the providing unit can suggest gluten-free meals. This allows the user to choose safe meals that take allergies into consideration.

[0127] The linking unit can also learn the user's food preferences and tastes and customize the menu information it proposes. For example, if the user likes Japanese food, the linking unit will preferentially propose Japanese food menus. Also, if the user is a vegetarian, the linking unit can also propose vegetarian menus. Furthermore, if the user likes a particular ingredient, the linking unit can also propose menus that include that ingredient. This allows the user to choose meals that suit their preferences.

[0128] The recipe unit can also adjust the difficulty of the suggested recipes taking into account the cooking skill level of the user. For example, if the user is a beginner, the recipe unit can suggest simple and basic recipes. If the user is an intermediate cook, the recipe unit can suggest slightly more complicated recipes. Furthermore, if the user is an advanced cook, the recipe unit can suggest recipes that require advanced techniques. This allows the user to choose recipes that suit their cooking skills.

[0129] The identification unit can also identify nutrients taking into account the genetic information of the user. For example, if the user has genetic poor absorption of vitamin D, the identification unit can suggest a diet rich in vitamin D. Also, if the user has genetic poor absorption of iron, the identification unit can also suggest a diet rich in iron. Furthermore, if the user has genetic slow metabolism of a particular nutrient, the identification unit can also suggest a diet for properly ingesting that nutrient. This allows the user to ingest optimal nutrients based on their genetic information.

[0130] The suggestion unit can also estimate the user's emotions and adjust the type of meal to be suggested based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a meal that has a relaxing effect. If the user is feeling happy, the suggestion unit can also suggest a meal that is suitable for a celebration. Furthermore, if the user is feeling sad, the suggestion unit can also suggest a meal that will lift your mood. This allows the user to choose a meal that matches their emotions.

[0131] The reception unit can also estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface. If the user is relaxed, the reception unit can provide an interface that allows detailed information to be input. Furthermore, if the user is in a hurry, the reception unit can provide an interface that allows quick input. This allows the user to input subjective symptoms in an interface that matches their emotion.

[0132] The identification unit can also estimate the user's emotions and adjust the nutrient identification method based on the estimated user's emotions. For example, when the user is stressed, the identification unit preferentially identifies nutrients that help reduce stress. When the user is relaxed, the identification unit can also preferentially identify nutrients that help maintain relaxation. Furthermore, when the user is tired, the identification unit can also preferentially identify nutrients that help recover from fatigue. In this way, by adjusting the nutrient identification method according to the user's emotions, more appropriate nutrients can be identified.

[0133] The providing unit can also estimate the user's emotions and adjust the display method of the nutrient information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple and highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the nutrient information according to the user's emotions.

[0134] The linking unit can also estimate the user's emotions and adjust the linking method of the menu information based on the estimated user's emotions. For example, if the user is feeling stressed, the linking unit can prioritize linking menu information that helps reduce stress. Also, if the user is relaxed, the linking unit can also prioritize linking menu information for maintaining relaxation. Furthermore, if the user is tired, the linking unit can also prioritize linking menu information that helps recover from fatigue. In this way, by adjusting the linking method of the menu information according to the user's emotions, it is possible to provide more appropriate menu information.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The reception unit inputs the subjective symptoms of the user. The subjective symptoms of the user include, but are not limited to, for example, headache, fatigue, stomach discomfort, etc. The reception unit allows the user to input the subjective symptoms using, for example, a smartphone or a personal computer. The reception unit can also input the subjective symptoms using voice input or a touch panel. Step 2: The identification unit uses AI to identify nutrients based on the subjective symptoms input by the reception unit. Examples of nutrients include, but are not limited to, vitamins, minerals, and proteins. For example, the identification unit uses AI to identify nutrients based on past data and medical knowledge. The identification unit can also identify nutrients by using AI to refer to the user's past diet history and health checkup results. Step 3: The suggestion unit uses AI to suggest meals based on the nutrients identified by the identification unit. Meals include, but are not limited to, breakfast, lunch, dinner, and snacks. For example, when the user eats at a convenience store or a chain restaurant, the suggestion unit suggests recommended menus in cooperation with menu information. In addition, the suggestion unit can also suggest recipes when the user selects to cook at home.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0138] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, provision unit, linkage unit, and recipe unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's subjective symptoms using the touch panel 38A or microphone 38B of the smart device 14. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI identifies nutrients based on the subjective symptoms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests meals based on the identified nutrients. The provision unit displays nutrient information of the proposed meals using, for example, the display 40A of the smart device 14. The linkage unit links, for example, with menu information of convenience stores and chain stores via the communication I / F 26 of the data processing device 12. The recipe unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes when home cooking is selected. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] Fig. 4 shows an example of 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.

[0149] 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.

[0150] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, provision unit, linkage unit, and recipe unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's subjective symptoms using the microphone 238 of the smart glasses 214. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI identifies nutrients based on the subjective symptoms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests meals based on the identified nutrients. The provision unit displays nutrient information of the proposed meals using, for example, the display of the smart glasses 214. The linkage unit links, for example, with menu information of convenience stores and chain stores via the communication I / F 26 of the data processing device 12. The recipe unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes when home cooking is selected. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] 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 performed in a secure state.

[0164] 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.

[0165] 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.

[0166] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0168] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] 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 voice indicating a user input for 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.

[0170] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, provision unit, linkage unit, and recipe unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can input the user's subjective symptoms using the microphone 238 of the headset type terminal 314. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the AI ​​identifies nutrients based on the subjective symptoms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests meals based on the identified nutrients. The provision unit displays nutrient information of the proposed meals using, for example, the display 343 of the headset type terminal 314. The linkage unit links, for example, with menu information of convenience stores and chain stores via the communication I / F 26 of the data processing device 12. The recipe unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes when self-cooking is selected. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0176] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] 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 the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] 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 performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0181] 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.

[0182] 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.

[0183] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0185] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] 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 a voice indicating a user input for 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.

[0187] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0189] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, provision unit, linkage unit, and recipe unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's subjective symptoms using the microphone 238 of the robot 414. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the AI ​​identifies nutrients based on the subjective symptoms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests meals based on the identified nutrients. The provision unit displays nutrient information of the proposed meals using, for example, the display of the robot 414. The linkage unit links, for example, with menu information of convenience stores and chain stores via the communication I / F 26 of the data processing device 12. The recipe unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes when home cooking is selected. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0197] 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.

[0198] 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 Universal Serial Bus (USB) 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.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] 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.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0203] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) A reception unit for inputting subjective symptoms of a user; An identification unit that identifies nutrients based on the subjective symptoms input by the reception unit; a suggestion unit that suggests a meal based on the nutrients identified by the identification unit; Equipped A system characterized by: (Appendix 2) A presentation unit is provided to provide nutritional information for the proposed meal. 2. The system of claim 1, (Appendix 3) Equipped with a linking section that links with menu information from convenience stores or chain restaurants 2. The system of claim 1, (Appendix 4) Equipped with a recipe section that offers recipe suggestions if you choose to cook at home 2. The system of claim 1, (Appendix 5) The identification unit is Identify nutrients based on historical data or medical knowledge 2. The system of claim 1, (Appendix 6) The suggestion unit, Vary your suggestions based on where you're dining 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust the timing of inputting subjective symptoms based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past subjective symptoms and select the appropriate input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering symptoms, filtering is performed based on the user's current life situation or areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotion is estimated, and a priority order of subjective symptoms to be input is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When entering symptoms, the system prioritizes relevant symptoms based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The reception unit is When a user enters their symptoms, the system analyzes their social media activity and inputs related symptoms. 2. The system of claim 1, (Appendix 13) The identification unit is Inferring user emotions and adjusting the nutrient identification method based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The identification unit is When identifying nutrients, the accuracy of identification is improved by referring to the user's past nutritional intake history. 2. The system of claim 1, (Appendix 15) The identification unit is When identifying nutrients, customize the specific measures based on the user's current living situation 2. The system of claim 1, (Appendix 16) The identification unit is Inferring a user's emotion and determining a priority order for nutrients to be identified based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The identification unit is During nutrient identification, appropriate nutrients are identified based on the user's geographic location information. 2. The system of claim 1, (Appendix 18) The identification unit is Analyzing users' social media activity to improve nutrient identification 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of nutrients 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the nutrient category. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, prioritize suggestions based on the timing of nutrient identification 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the order of suggestions based on nutrient relevance 2. The system of claim 1, (Appendix 25) The providing unit is Inferring user emotion and adjusting the way nutrient information is displayed based on the estimated user emotion 3. The system of claim 2, (Appendix 26) The providing unit is When providing nutritional information, provide appropriate information based on the user's past nutritional intake history 3. The system of claim 2, (Appendix 27) The providing unit is Estimating a user's emotions and prioritizing nutrient information based on the estimated user's emotions 3. The system of claim 2, (Appendix 28) The providing unit is When providing nutrition information, the system provides optimal information by taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 29) The linking portion is To estimate a user's emotion and adjust a menu information linking method based on the estimated user's emotion. 4. The system of claim 3. (Appendix 30) The linking portion is When linking menu information, the appropriate linking method is selected based on the user's past meal history. 4. The system of claim 3. (Appendix 31) The linking portion is To estimate a user's emotion and determine a priority order of menu information based on the estimated user's emotion. 4. The system of claim 3. (Appendix 32) The linking portion is When linking menu information, the optimal linking method is selected taking into account the user's geographical location information. 4. The system of claim 3. (Appendix 33) The recipe section includes: Infer user emotions and adjust recipe suggestions based on the inferred user emotions 5. The system of claim 4. (Appendix 34) The recipe section includes: When suggesting recipes, the system provides the most suitable recipes by referring to the user's past cooking history. 5. The system of claim 4. (Appendix 35) The recipe section includes: Infer user sentiment and prioritize recipes based on the inferred sentiment 5. The system of claim 4. (Appendix 36) The recipe section includes: When suggesting recipes, the app takes into account the user's geographic location to provide the best recipes. 5. The system of claim 4. (Appendix 37) The identification unit is Inferring user emotions and adjusting the nutrient identification method based on the estimated user emotions 2. The system of claim 1, (Appendix 38) The identification unit is When identifying nutrients, the accuracy of identification is improved by referring to the user's past nutritional intake history. 2. The system of claim 1, (Appendix 39) The identification unit is When identifying nutrients, customize the specific measures based on the user's current living situation 2. The system of claim 1, (Appendix 40) The identification unit is Inferring a user's emotion and determining a priority order for nutrients to be identified based on the estimated user's emotion 2. The system of claim 1, (Appendix 41) The identification unit is When identifying nutrients, the geographic location of the user is taken into consideration to identify the most suitable nutrients. 2. The system of claim 1, (Appendix 42) The identification unit is Analyzing users' social media activity to improve nutrient identification 2. The system of claim 1, (Appendix 43) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 44) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of nutrients 2. The system of claim 1, (Appendix 45) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the nutrient category. 2. The system of claim 1, (Appendix 46) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 47) The suggestion unit, When making suggestions, prioritize suggestions based on the timing of nutrient identification 2. The system of claim 1, (Appendix 48) The suggestion unit, When making suggestions, adjust the order of suggestions based on nutrient relevance 2. The system of claim 1, [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting subjective symptoms of a user; An identification unit that identifies nutrients based on the subjective symptoms input by the reception unit; a suggestion unit that suggests a meal based on the nutrients identified by the identification unit; Equipped A system characterized by:

2. A presentation unit is provided to provide nutritional information for the proposed meal.

2. The system of claim 1 .

3. Equipped with a linking section that links with menu information from convenience stores or chain restaurants 2. The system of claim 1 .

4. Equipped with a recipe section that offers recipe suggestions if you choose to cook at home 2. The system of claim 1 .

5. The identification unit is Identify nutrients based on historical data or medical knowledge 2. The system of claim 1 .

6. The suggestion unit, Vary your suggestions based on where you're dining 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust the timing of inputting subjective symptoms based on the estimated emotion of the user.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past subjective symptoms and select the appropriate input method 2. The system of claim 1 .

9. The reception unit is When entering symptoms, filtering is performed based on the user's current life situation or areas of interest 2. The system of claim 1 .

10. The reception unit is A user's emotion is estimated, and a priority order of subjective symptoms to be input is determined based on the estimated user's emotion.

2. The system of claim 1 .

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