System

Abstract

To provide a system for proposing necessary nutrients and concrete food names on the basis of health diagnosis data.SOLUTION: A system includes a collection part, an analysis part, a proposal part, and a database part. The collection part collects health diagnosis data of a user. The analysis part analyzes the data collected by the collection part. The proposal part proposes necessary nutrients on the basis of an analysis result obtained by the analysis part. The database part manages a food database for proposing concrete food names.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] With conventional technology, it was difficult to accurately suggest necessary nutrients based on health checkup data, and it was not possible to suggest specific food names.

[0005] The system according to the embodiment aims to suggest necessary nutrients and specific food names based on health checkup data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a database unit. The collection unit collects health checkup data of a user. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The database unit manages a food database for suggesting specific food names. Effect of the Invention

[0007] The system according to the embodiment can suggest necessary nutrients and specific food names based on health checkup data. [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 health management and diet support system according to the embodiment of the present invention is a system in which AI supports health management and dieting by collecting health checkup data of a user. This system collects health checkup data of a user, and AI analyzes the data to evaluate the user's health and nutritional state. Furthermore, AI tracks the nutrients ingested based on the user's diet history and reflects them in the next proposal. AI also provides a diet plan according to the user's goal. This allows the user to continue eating according to their physical condition and nutritional state. By proposing specific food names, the user can easily take in the necessary nutrients and live a healthy life. For example, a collection unit that collects the user's health checkup data and an analysis unit that analyzes the collected data are required. Next, a proposal unit that proposes the necessary nutrients based on the analysis results is required. Furthermore, a database unit that manages a food database for proposing specific food names is also required. The analysis unit analyzes the data collected by the collection unit, the proposal unit makes a proposal based on the analysis results, and the database unit provides specific food names. Furthermore, a tracking unit that tracks the user's diet history and a plan unit that provides a diet plan are also included. This allows the health management and diet support system to provide appropriate meals and diet plans based on the user's physical condition and nutritional state.

[0029] The health management and diet support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a database unit. The collection unit collects health checkup data of the user. The health checkup data of the user includes, for example, blood test results, weight, height, blood pressure, etc., but is not limited to such examples. The collection unit, for example, acquires the health checkup data of the user from an electronic medical record system. The collection unit can also collect health checkup data input by the user. Furthermore, the collection unit can also collect data acquired from a wearable device. For example, the collection unit acquires the blood test results of the user from an electronic medical record system, and the user inputs the weight and height. Data such as heart rate and number of steps are collected from the wearable device. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the health condition of the user using statistical analysis. The analysis unit can also predict the health risk of the user using machine learning algorithms. Furthermore, the analysis unit can extract patterns related to the health condition of the user using data mining technology. For example, the analysis unit evaluates the health condition based on the user's blood test results as a statistical analysis. As a machine learning algorithm, the unit learns the user's past health check data and predicts future health risks. As a data mining technique, the unit extracts patterns related to the user's health condition and uses them for health management. The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are made based on nutrients such as vitamins, minerals, and proteins, but are not limited to such examples. For example, the suggestion unit suggests the intake of vitamin C based on the user's health condition. The suggestion unit can also suggest the intake of minerals based on the user's nutritional condition. Furthermore, the suggestion unit can also suggest the intake of protein based on the user's goal. For example, the suggestion unit suggests the intake of vitamin C based on the user's health condition. The suggestion unit suggests the intake of minerals based on the user's nutritional condition. The suggestion unit suggests the intake of protein based on the user's goal. The database unit manages a food database for suggesting specific food names.The food database includes, for example, information such as food name, nutritional components, and calories, but is not limited to such examples. For example, the database unit registers food names and their nutritional components in the food database. The database unit can also register calorie information in the food database. Furthermore, the database unit can also register images of foods in the food database. For example, the database unit registers food names and their nutritional components in the food database. Registers calorie information. Registers images of foods. In this way, the health management and diet support system according to the embodiment can accurately suggest necessary nutrients based on the user's health checkup data and provide specific food names, thereby supporting the user's health management and diet.

[0030] The collection unit collects the user's health checkup data. The user's health checkup data includes, for example, blood test results, weight, height, blood pressure, etc., but is not limited to such examples. For example, the collection unit acquires the user's health checkup data from an electronic medical record system. The collection unit can also collect health checkup data input by the user. Furthermore, the collection unit can collect data acquired from a wearable device. For example, the collection unit acquires the user's blood test results from the electronic medical record system, and the user inputs the user's weight and height. Data such as heart rate and number of steps are collected from the wearable device. Specifically, data acquisition from the electronic medical record system is performed through a secure API, and data is automatically collected with the user's consent. The data entered by the user is designed to be easily entered through a dedicated application or web portal, and the entered data is immediately reflected in the system. Data collection from the wearable device is performed in real time via Bluetooth (registered trademark) or Wi-Fi, and data recorded by the device is automatically transmitted to a cloud server. This allows the collection unit to collect comprehensive health information from various data sources and grasp the user's health condition in detail. Furthermore, the collection unit also has the function of checking data consistency and detecting abnormal values ​​to ensure data quality. For example, if an abnormally high blood pressure value or a sudden change in weight is detected, an alert can be issued to prompt the user to check. This enables the collection unit to provide highly reliable data, building a foundation for the analysis unit and proposal unit to perform accurate analysis and proposals.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit evaluates the user's health condition using statistical analysis. The analysis unit can also predict the user's health risk using a machine learning algorithm. Furthermore, the analysis unit can extract patterns related to the user's health condition using data mining technology. For example, the analysis unit evaluates the health condition based on the user's blood test results as statistical analysis. As a machine learning algorithm, the analysis unit learns the user's past health checkup data and predicts future health risks. As a data mining technology, the analysis unit extracts patterns related to the user's health condition and uses them for health management. Specifically, in the statistical analysis, the analysis unit compares the user's blood test results with standard values ​​to detect abnormal values ​​and evaluate the health condition. For example, if blood sugar levels or cholesterol levels exceed the standard values, it is determined that the risk of diabetes or cardiovascular disease is high. In the machine learning algorithm, past health checkup data is used to predict the user's health risk. For example, a model learned from past data can be used to predict future increases in blood pressure and weight gain, and measures can be taken early. Data mining technology extracts patterns related to the user's health condition and uses them for health management. For example, it is possible to analyze the impact of a specific diet or exercise habit on the health condition and create an individual health management plan. This allows the analysis unit to analyze the collected data from multiple angles, evaluate the user's health condition in detail, and predict risks. Furthermore, the analysis unit also has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, graphs and charts can be used to visually display changes in health condition and risk trends, allowing the user to intuitively understand his or her own health condition. This allows the analysis unit to support the user's health management and provide information for taking appropriate measures.

[0032] The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are made based on nutrients such as vitamins, minerals, and proteins, but are not limited to such examples. For example, the suggestion unit suggests the intake of vitamin C based on the health condition of the user. The suggestion unit can also suggest the intake of minerals based on the nutritional condition of the user. Furthermore, the suggestion unit can also suggest the intake of protein based on the user's goal. For example, the suggestion unit suggests the intake of vitamin C based on the health condition of the user. The suggestion unit suggests the intake of minerals based on the nutritional condition of the user. The suggestion unit suggests the intake of protein based on the user's goal. Specifically, the suggestion unit evaluates the current health condition and nutritional condition of the user based on the data provided by the analysis unit, and identifies necessary nutrients. For example, if there is a shortage of vitamin C, the suggestion unit suggests the intake of foods and supplements that are rich in vitamin C. Furthermore, if there is a shortage of minerals, the suggestion unit suggests the intake of foods and supplements that are rich in minerals. Furthermore, the suggestion unit suggests the intake of protein based on the user's goal. For example, for a user who wants to increase muscle mass, the suggestion unit suggests the intake of foods and supplements that are rich in protein. The suggestion unit suggests specific foods and supplements taking into account the lifestyle and preferences of the user. For example, if the user is a vegetarian, the suggestion unit suggests foods that are rich in vegetable protein. Also, if the user leads a busy life, the suggestion unit suggests supplements that can be easily taken. In this way, the suggestion unit can suggest nutrients according to the individual needs of the user and support health management and dieting. Furthermore, the suggestion unit uses visual displays and explanatory text to present the suggested content to the user in an easy-to-understand manner. For example, images of foods and graphs of nutritional components are displayed so that the user can intuitively understand the suggested content. In this way, the suggestion unit makes it easier for the user to put the suggested content into practice, thereby improving the effectiveness of health management and dieting.

[0033] The database unit manages a food database for suggesting specific food names. The food database includes, for example, information such as food names, nutritional components, and calories, but is not limited to such examples. For example, the database unit registers food names and their nutritional components in the food database. In addition, the database unit can also register calorie information in the food database. In addition, the database unit can also register images of foods in the food database. For example, the database unit registers food names and their nutritional components in the food database. Calorie information is registered. An image of a food is registered. Specifically, the database unit accurately manages nutritional component and calorie information of foods to enable a user to quickly provide information required by the user. For example, a database that details food names, nutritional components, and calorie information is constructed to make it easier for a user to search and filter. In addition, by registering images of foods, it is easier for a user to visually select foods. The database unit periodically updates the contents of the database and adds new foods and nutritional information. For example, when a new food appears on the market, nutritional component and calorie information of the food is quickly added to the database. In addition, when nutritional component and calorie information of an existing food is changed, the information is updated. This allows the database unit to always provide the latest information, enabling the user to select foods based on accurate information. Furthermore, the database unit also has a function of making individual suggestions taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, suggestions are made that exclude that food. Also, based on the user's preferences, preferred foods are preferentially suggested. This allows the database unit to make suggestions according to the individual needs of the user, supporting health management and dieting.

[0034] The tracking unit can track the user's meal history. The tracking unit, for example, records the type and amount of food consumed by the user. For example, the tracking unit can input the name and amount of food eaten by the user. The tracking unit can also have the user take a photo of the meal and record the meal history based on the photo. For example, the tracking unit inputs the name and amount of food eaten by the user. The user takes a photo of the meal and records the meal history based on the photo. The tracking unit can also record the seasonings and cooking methods used by the user. For example, the tracking unit inputs the seasonings and cooking methods used by the user. This allows the tracking unit to accurately grasp the user's meal history and reflect it in the next proposal. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the meal data input by the user to the generation AI and cause the generation AI to execute the recording of the meal history.

[0035] The plan unit can provide a diet plan. The plan unit can provide, for example, a diet plan according to the user's goal. For example, the plan unit can suggest a dietary restriction and an exercise program based on the weight loss goal set by the user. The plan unit can also provide an appropriate diet plan based on the user's health condition. For example, the plan unit can suggest a dietary restriction and an exercise program based on the user's health check data. Furthermore, the plan unit can adjust the diet plan based on the user's lifestyle. For example, the plan unit can provide a reasonable diet plan taking into account the user's lifestyle. As a result, the plan unit can support the user's health management by providing a diet plan according to the user's goal. Some or all of the above-mentioned processing in the plan unit can be performed, for example, using AI or without AI. For example, the plan unit can input the user's goal and health condition to the generation AI and cause the generation AI to execute the generation of the diet plan.

[0036] The collection unit can analyze the user's past health checkup data and select the optimal collection method. The collection unit, for example, selects the most effective collection method from the user's past health checkup data. For example, the collection unit analyzes the user's past blood test results and selects the optimal collection method. The collection unit can also adjust the collection frequency based on the user's past health checkup data. For example, the collection unit analyzes the user's past health checkup data and adjusts the collection frequency. Furthermore, the collection unit can analyze the user's past health checkup data and select a collection method that focuses on a specific health index. For example, the collection unit analyzes the user's past health checkup data and selects a collection method that focuses on a specific health index. As a result, the collection unit can efficiently collect data by selecting the optimal collection method based on the past health checkup data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health checkup data to the generation AI and cause the generation AI to select the optimal collection method.

[0037] The collection unit can perform filtering based on the user's current living situation and health condition when collecting the health checkup data. The collection unit, for example, takes into account the user's current living situation and collects appropriate health checkup data. For example, when the user is busy, the collection unit collects simple health checkup data. The collection unit can also collect only necessary data based on the user's current health condition. For example, when the user is healthy, the collection unit collects basic health checkup data. Furthermore, the collection unit can filter the data to be collected by taking into account the user's lifestyle habits. For example, when the user is exercising, the collection unit collects data related to exercise. In this way, the collection unit can collect only necessary data by filtering the data according to the user's living situation and health condition. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and health condition to the generation AI and cause the generation AI to perform data filtering.

[0038] When collecting health checkup data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific area, the collection unit preferentially collects health data related to the area. For example, when the user is in a high altitude, the collection unit collects health data related to the high altitude. The collection unit can also collect data taking into account health risks specific to the area based on the geographical location information of the user. For example, when the user is in a tropical area, the collection unit collects data related to tropical diseases. Furthermore, when the user is traveling, the collection unit can also preferentially collect data related to health risks at the travel destination. For example, when the user is traveling, the collection unit collects data related to health risks at the travel destination. In this way, the collection unit can collect data related to health risks specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting health checkup data. For example, the collection unit analyzes health-related posts from the user's social media activity and collects related data. For example, the collection unit collects health-related information posted by the user on social media. The collection unit can also identify health-related concerns based on the user's social media activity and collect related data. For example, the collection unit collects data related to health topics in which the user has shown interest on social media. Furthermore, the collection unit can analyze the user's social media activity and collect data related to health risks. For example, the collection unit collects information related to health risks shared by the user on social media. In this way, the collection unit can collect data related to health-related concerns and risks by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup data during the analysis. The analysis unit performs detailed analysis on important health indicators, for example. For example, the analysis unit performs detailed analysis on blood test data. The analysis unit can also perform simple analysis on general health indicators. For example, the analysis unit performs simple analysis on weight and height. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health condition. For example, the analysis unit performs simple analysis when the user is healthy, and performs detailed analysis when the user has a health risk. Thereby, the analysis unit can perform analysis efficiently by adjusting the level of detail of the analysis according to the importance of the health checkup data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the health checkup data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the medical examination data during analysis. For example, the analysis unit applies a specific analysis algorithm to blood test data. For example, the analysis unit applies regression analysis to the blood test data. The analysis unit can also apply another analysis algorithm to electrocardiogram data. For example, the analysis unit applies clustering to the electrocardiogram data. Furthermore, the analysis unit can also apply an image analysis algorithm to image diagnostic data. For example, the analysis unit applies deep learning to the image diagnostic data. As a result, the analysis unit applies an appropriate analysis algorithm depending on the category of the medical examination data, thereby improving the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the medical examination data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] The analysis unit can determine the priority of the analysis based on the submission time of the health checkup data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest health checkup data. For example, the analysis unit prioritizes the analysis of the latest blood test data. The analysis unit can also postpone data submitted earlier. For example, the analysis unit postpones blood test data submitted earlier. Furthermore, the analysis unit can adjust the priority of the analysis based on the submission time. For example, the analysis unit adjusts the priority of the analysis based on the submission time. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the submission time of the health checkup data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time of the health checkup data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the medical examination data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant blood test data. The analysis unit can also postpone data with low relevance. For example, the analysis unit postpones blood test data with low relevance. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the medical examination data. For example, the analysis unit adjusts the order of analysis based on the relevance of the medical examination data. As a result, the analysis unit can prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of the medical examination data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the medical examination data to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. For example, the suggestion unit makes a detailed proposal for an important nutrient. For example, the suggestion unit makes a detailed proposal for vitamin C. The suggestion unit can also make a simple proposal for a common nutrient. For example, the suggestion unit makes a simple proposal for a common vitamin. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the health condition of the user. For example, the suggestion unit makes a simple proposal when the user is healthy, and makes a detailed proposal when the user has a health risk. Thereby, the suggestion unit can make an efficient proposal by adjusting the level of detail of the proposal according to the importance of the nutrient. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the nutrient to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0045] The suggestion unit can apply different suggestion algorithms depending on the category of nutrients when making a suggestion. For example, the suggestion unit applies a specific suggestion algorithm to vitamins. For example, the suggestion unit applies a recommendation system to vitamins. The suggestion unit can also apply another suggestion algorithm to minerals. For example, the suggestion unit applies a rule-based suggestion to minerals. Furthermore, the suggestion unit can apply a different suggestion algorithm to proteins. For example, the suggestion unit applies a machine learning algorithm to proteins. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the category of nutrients, thereby improving the accuracy of the suggestion. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of nutrients to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0046] The suggestion unit can determine the priority of the suggestion based on the submission time of the nutrient at the time of suggestion. For example, the suggestion unit preferentially suggests the latest nutrient data. For example, the suggestion unit preferentially suggests the latest vitamin data. The suggestion unit can also postpone data submitted earlier. For example, the suggestion unit postpones vitamin data submitted earlier. Furthermore, the suggestion unit can adjust the priority of the suggestion based on the submission time. For example, the suggestion unit adjusts the priority of the suggestion based on the submission time. Thereby, the suggestion unit can preferentially suggest the latest nutrient data by determining the priority of the suggestion based on the submission time of the nutrient. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the submission time of the nutrient to the generation AI and cause the generation AI to execute the determination of the priority of the suggestion.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant nutrients. For example, the suggestion unit preferentially suggests highly relevant vitamins. The suggestion unit can also postpone less relevant nutrients. For example, the suggestion unit postpones less relevant vitamins. Furthermore, the suggestion unit can also adjust the order of suggestions based on the relevance of nutrients. For example, the suggestion unit adjusts the order of suggestions based on the relevance of nutrients. As a result, the suggestion unit can preferentially suggest highly relevant nutrients by adjusting the order of suggestions based on the relevance of nutrients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of nutrients to the generation AI and cause the generation AI to adjust the order of suggestions.

[0048] The database unit can adjust the level of detail of data based on the importance of the nutrient data of food when managing the database. For example, the database unit manages detailed data for important nutrient data. For example, the database unit manages detailed data for vitamin C. The database unit can also manage simple data for general nutrient data. For example, the database unit manages simple data for general vitamins. Furthermore, the database unit can adjust the level of detail of data according to the health condition of the user. For example, the database unit manages simple data when the user is healthy, and manages detailed data when the user has a health risk. This allows the database unit to efficiently manage data by adjusting the level of detail of data according to the importance of the nutrient data of food. Part or all of the above-mentioned processing in the database unit may be performed, for example, using AI, or may be performed without using AI. For example, the database unit can input the importance of nutrient data to the generation AI and cause the generation AI to adjust the level of detail of the data.

[0049] The database unit can apply different data management algorithms according to the food category when managing the database. For example, the database unit applies a specific data management algorithm to vegetables. For example, the database unit applies an index creation algorithm to vegetables. The database unit can also apply a different data management algorithm to meat. For example, the database unit applies a data compression algorithm to meat. Furthermore, the database unit can apply a different data management algorithm to seafood. For example, the database unit applies a data cleansing algorithm to seafood. As a result, the database unit applies an appropriate data management algorithm according to the food category, thereby improving the accuracy of data management. Part or all of the above-mentioned processing in the database unit may be performed using, for example, AI, or may be performed without using AI. For example, the database unit can input the food category to the generation AI and cause the generation AI to apply the data management algorithm.

[0050] The database unit can determine the priority of data based on the submission time of food when managing the database. For example, the database unit preferentially manages the latest food data. For example, the database unit preferentially manages the latest vegetable data. The database unit can also postpone data submitted earlier. For example, the database unit postpones vegetable data submitted earlier. Furthermore, the database unit can also adjust the priority of data based on the submission time. For example, the database unit adjusts the priority of data based on the submission time. Thereby, the database unit can preferentially manage the latest food data by determining the priority of data based on the submission time of food. Part or all of the above-mentioned processing in the database unit may be performed using, for example, AI, or may be performed without using AI. For example, the database unit can input the submission time of food to the generation AI and cause the generation AI to execute the determination of the priority of data.

[0051] The database unit can adjust the order of data based on the relevance of food when managing the database. For example, the database unit preferentially manages highly relevant food data. For example, the database unit preferentially manages highly relevant vegetable data. The database unit can also postpone low-relevance food data. For example, the database unit postpones low-relevance vegetable data. Furthermore, the database unit can also adjust the order of data based on the relevance of food data. For example, the database unit adjusts the order of data based on the relevance of food data. In this way, the database unit can preferentially manage highly relevant food data by adjusting the order of data based on the relevance of food. A part or all of the above-mentioned processing in the database unit may be performed, for example, using AI, or may be performed without using AI. For example, the database unit can input the relevance of food to the generation AI and cause the generation AI to adjust the order of data.

[0052] The tracking unit can select the optimal tracking method by analyzing the user's past meal history during tracking. The tracking unit, for example, selects the most effective tracking method from the user's past meal history. For example, the tracking unit analyzes the user's past meal history and selects the optimal tracking method. The tracking unit can also adjust the tracking frequency based on the user's past meal history. For example, the tracking unit analyzes the user's past meal history and adjusts the tracking frequency. Furthermore, the tracking unit can analyze the user's past meal history and select a tracking method that emphasizes a specific nutrient. For example, the tracking unit analyzes the user's past meal history and selects a tracking method that emphasizes a specific nutrient. As a result, the tracking unit can efficiently track the meal history by selecting the optimal tracking method based on the user's past meal history. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past meal history to the generation AI and cause the generation AI to select the optimal tracking method.

[0053] The tracking unit can prioritize tracking of highly relevant history in consideration of the geographical location information of the user during tracking. For example, when the user is in a specific area, the tracking unit prioritizes tracking of a meal history related to the area. For example, when the user is in a high altitude, the tracking unit tracks a meal history related to the high altitude. The tracking unit can also track a history taking into account a region-specific dietary risk based on the geographical location information of the user. For example, when the user is in a tropical area, the tracking unit tracks a dietary history related to a tropical disease. Furthermore, when the user is traveling, the tracking unit can also prioritize tracking of a history related to a dietary risk at a travel destination. For example, when the user is traveling, the tracking unit tracks a history related to a dietary risk at a travel destination. In this way, the tracking unit can track a history related to a region-specific dietary risk by considering the geographical location information of the user. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute tracking of a highly relevant history.

[0054] When providing a plan, the plan unit can provide an optimal plan by referring to the user's past diet history. The plan unit, for example, provides the most effective plan from the user's past diet history. For example, the plan unit analyzes the user's past diet history and provides an optimal plan. The plan unit can also adjust the level of detail of the plan based on the user's past diet history. For example, the plan unit analyzes the user's past diet history and adjusts the level of detail of the plan. Furthermore, the plan unit can analyze the user's past diet history and provide a plan that focuses on a specific goal. For example, the plan unit analyzes the user's past diet history and provides a plan that focuses on a specific goal. As a result, the plan unit can efficiently support dieting by providing an optimal plan based on the user's past diet history. Part or all of the above-mentioned processing in the plan unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan unit can input the user's past diet history into the generation AI and cause the generation AI to provide an optimal plan.

[0055] When providing a plan, the plan unit can provide an optimal plan by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the plan unit provides a diet plan related to the area. For example, when the user is at high altitude, the plan unit provides a meal plan suitable for high altitude. The plan unit can also provide a plan that takes into consideration region-specific dietary risks based on the user's geographical location information. For example, when the user is in a tropical area, the plan unit provides a meal plan that is useful for preventing tropical diseases. Furthermore, when the user is traveling, the plan unit can also provide a plan related to dietary risks at the travel destination. For example, when the user is traveling, the plan unit provides a plan corresponding to the dietary risks at the travel destination. In this way, the plan unit can provide a plan related to region-specific dietary risks by taking into consideration the geographical location information of the user. Part or all of the above-mentioned processing in the plan unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan unit can input the user's geographical location information to the generation AI and cause the generation AI to provide an optimal plan.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When analyzing the user's health checkup data, the analysis unit can adjust the analysis results by taking into account the user's lifestyle habit data. For example, the analysis unit evaluates health risks by taking into account the user's exercise habits and sleep patterns. The analysis unit can also evaluate nutritional balance by taking into account the user's eating patterns. Furthermore, the analysis unit can make suggestions for stress management by taking into account the user's stress level. In this way, the analysis unit can provide more accurate analysis results by taking into account the user's lifestyle habit data.

[0058] The suggestion unit can suggest an exercise plan that matches the user's lifestyle based on the user's health check data. For example, if the user lives a desk-based lifestyle, the suggestion unit can suggest simple exercises that can be done at the office. If the user prefers outdoor activities, the suggestion unit can also suggest exercise plans such as hiking and jogging. Furthermore, the suggestion unit can provide a plan that adjusts the exercise intensity and frequency in consideration of the user's exercise history. In this way, the suggestion unit can support health management by providing an exercise plan that matches the user's lifestyle.

[0059] When collecting the user's health checkup data, the collection unit can adjust the collection method taking into account the user's living environment data. For example, if the user lives in an urban area, the collection unit collects data related to city-specific health risks. Also, if the user lives in a rural area, the collection unit can collect data related to rural-specific health risks. Furthermore, the collection unit can adjust the collection frequency and collection items based on the user's living environment. In this way, the collection unit can collect more appropriate health checkup data by taking into account the user's living environment data.

[0060] When analyzing the user's health checkup data, the analysis unit can adjust the analysis results by taking into account the user's genetic information. For example, the analysis unit can evaluate the risk of a particular disease by taking into account the user's family history and genetic risk. The analysis unit can also provide personalized nutritional advice based on the user's genetic information. Furthermore, the analysis unit can adjust the exercise plan based on the user's genetic information. In this way, the analysis unit can provide more accurate analysis results by taking into account the user's genetic information.

[0061] The suggestion unit can suggest a health management plan that matches the user's hobbies and interests based on the user's health checkup data. For example, if the user's hobby is cooking, the suggestion unit can suggest healthy recipes. Also, if the user's hobby is gardening, the suggestion unit can suggest an exercise plan through gardening. Furthermore, the suggestion unit can suggest a stress management method based on the user's hobbies and interests. In this way, the suggestion unit can support the user's health management by providing a health management plan that matches the user's hobbies and interests.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects the user's health checkup data. The user's health checkup data includes blood test results, weight, height, blood pressure, etc. The collection unit acquires data from the electronic medical record system, as well as data entered by the user and data from the wearable device. For example, blood test results are acquired from the electronic medical record system, weight and height are entered by the user, and data such as heart rate and number of steps are collected from the wearable device. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit evaluates the user's health condition using statistical analysis, predicts health risks using machine learning algorithms, and extracts patterns related to the health condition using data mining techniques. Step 3: The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are based on nutrients such as vitamins, minerals, and proteins. For example, the suggestion unit suggests intake of vitamin C based on the user's health condition, suggests intake of minerals based on the nutritional condition, and suggests intake of protein based on the goal. Step 4: The database unit manages a food database for suggesting specific food names. The food database includes information such as food names, nutritional components, and calories. For example, food names, their nutritional components, calorie information, and food images are registered.

[0064] (Example 2) The health management and diet support system according to the embodiment of the present invention is a system in which AI supports health management and dieting by collecting health checkup data of a user. This system collects health checkup data of a user, and AI analyzes the data to evaluate the user's health and nutritional state. Furthermore, AI tracks the nutrients ingested based on the user's diet history and reflects them in the next proposal. AI also provides a diet plan according to the user's goal. This allows the user to continue eating according to their physical condition and nutritional state. By proposing specific food names, the user can easily take in the necessary nutrients and live a healthy life. For example, a collection unit that collects the user's health checkup data and an analysis unit that analyzes the collected data are required. Next, a proposal unit that proposes the necessary nutrients based on the analysis results is required. Furthermore, a database unit that manages a food database for proposing specific food names is also required. The analysis unit analyzes the data collected by the collection unit, the proposal unit makes a proposal based on the analysis results, and the database unit provides specific food names. Furthermore, a tracking unit that tracks the user's diet history and a plan unit that provides a diet plan are also included. This allows the health management and diet support system to provide appropriate meals and diet plans based on the user's physical condition and nutritional state.

[0065] The health management and diet support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a database unit. The collection unit collects health checkup data of the user. The health checkup data of the user includes, for example, blood test results, weight, height, blood pressure, etc., but is not limited to such examples. The collection unit, for example, acquires the health checkup data of the user from an electronic medical record system. The collection unit can also collect health checkup data input by the user. Furthermore, the collection unit can also collect data acquired from a wearable device. For example, the collection unit acquires the blood test results of the user from an electronic medical record system, and the user inputs the weight and height. Data such as heart rate and number of steps are collected from the wearable device. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit evaluates the health condition of the user using statistical analysis. The analysis unit can also predict the health risk of the user using machine learning algorithms. Furthermore, the analysis unit can extract patterns related to the health condition of the user using data mining technology. For example, the analysis unit evaluates the health condition based on the user's blood test results as a statistical analysis. As a machine learning algorithm, the unit learns the user's past health check data and predicts future health risks. As a data mining technique, the unit extracts patterns related to the user's health condition and uses them for health management. The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are made based on nutrients such as vitamins, minerals, and proteins, but are not limited to such examples. For example, the suggestion unit suggests the intake of vitamin C based on the user's health condition. The suggestion unit can also suggest the intake of minerals based on the user's nutritional condition. Furthermore, the suggestion unit can also suggest the intake of protein based on the user's goal. For example, the suggestion unit suggests the intake of vitamin C based on the user's health condition. The suggestion unit suggests the intake of minerals based on the user's nutritional condition. The suggestion unit suggests the intake of protein based on the user's goal. The database unit manages a food database for suggesting specific food names.The food database includes, for example, information such as food name, nutritional components, and calories, but is not limited to such examples. For example, the database unit registers food names and their nutritional components in the food database. The database unit can also register calorie information in the food database. Furthermore, the database unit can also register images of foods in the food database. For example, the database unit registers food names and their nutritional components in the food database. Registers calorie information. Registers images of foods. In this way, the health management and diet support system according to the embodiment can accurately suggest necessary nutrients based on the user's health checkup data and provide specific food names, thereby supporting the user's health management and diet.

[0066] The collection unit collects the user's health checkup data. The user's health checkup data includes, for example, blood test results, weight, height, blood pressure, etc., but is not limited to such examples. For example, the collection unit acquires the user's health checkup data from an electronic medical record system. The collection unit can also collect health checkup data input by the user. Furthermore, the collection unit can collect data acquired from a wearable device. For example, the collection unit acquires the user's blood test results from the electronic medical record system, and the user inputs the user's weight and height. Data such as heart rate and number of steps are collected from the wearable device. Specifically, data acquisition from the electronic medical record system is performed through a secure API, and data is automatically collected with the user's consent. The data entered by the user is designed to be easily entered through a dedicated application or web portal, and the entered data is immediately reflected in the system. Data collection from the wearable device is performed in real time via Bluetooth or Wi-Fi, and data recorded by the device is automatically transmitted to a cloud server. This allows the collection unit to collect comprehensive health information from various data sources and grasp the user's health condition in detail. Furthermore, the collection unit also has the function of checking data consistency and detecting abnormal values ​​to ensure data quality. For example, if an abnormally high blood pressure value or a sudden change in weight is detected, an alert can be issued to prompt the user to check. This enables the collection unit to provide highly reliable data, building a foundation for the analysis unit and proposal unit to perform accurate analysis and proposals.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit evaluates the user's health condition using statistical analysis. The analysis unit can also predict the user's health risk using a machine learning algorithm. Furthermore, the analysis unit can extract patterns related to the user's health condition using data mining technology. For example, the analysis unit evaluates the health condition based on the user's blood test results as statistical analysis. As a machine learning algorithm, the analysis unit learns the user's past health checkup data and predicts future health risks. As a data mining technology, the analysis unit extracts patterns related to the user's health condition and uses them for health management. Specifically, in the statistical analysis, the analysis unit compares the user's blood test results with standard values ​​to detect abnormal values ​​and evaluate the health condition. For example, if blood sugar levels or cholesterol levels exceed the standard values, it is determined that the risk of diabetes or cardiovascular disease is high. In the machine learning algorithm, past health checkup data is used to predict the user's health risk. For example, a model learned from past data can be used to predict future increases in blood pressure and weight gain, and measures can be taken early. Data mining technology extracts patterns related to the user's health condition and uses them for health management. For example, it is possible to analyze the impact of a specific diet or exercise habit on the health condition and create an individual health management plan. This allows the analysis unit to analyze the collected data from multiple angles, evaluate the user's health condition in detail, and predict risks. Furthermore, the analysis unit also has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, graphs and charts can be used to visually display changes in health condition and risk trends, allowing the user to intuitively understand his or her own health condition. This allows the analysis unit to support the user's health management and provide information for taking appropriate measures.

[0068] The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are made based on nutrients such as vitamins, minerals, and proteins, but are not limited to such examples. For example, the suggestion unit suggests the intake of vitamin C based on the health condition of the user. The suggestion unit can also suggest the intake of minerals based on the nutritional condition of the user. Furthermore, the suggestion unit can also suggest the intake of protein based on the user's goal. For example, the suggestion unit suggests the intake of vitamin C based on the health condition of the user. The suggestion unit suggests the intake of minerals based on the nutritional condition of the user. The suggestion unit suggests the intake of protein based on the user's goal. Specifically, the suggestion unit evaluates the current health condition and nutritional condition of the user based on the data provided by the analysis unit, and identifies necessary nutrients. For example, if there is a shortage of vitamin C, the suggestion unit suggests the intake of foods and supplements that are rich in vitamin C. Furthermore, if there is a shortage of minerals, the suggestion unit suggests the intake of foods and supplements that are rich in minerals. Furthermore, the suggestion unit suggests the intake of protein based on the user's goal. For example, for a user who wants to increase muscle mass, the suggestion unit suggests the intake of foods and supplements that are rich in protein. The suggestion unit suggests specific foods and supplements taking into account the lifestyle and preferences of the user. For example, if the user is a vegetarian, the suggestion unit suggests foods that are rich in vegetable protein. Also, if the user leads a busy life, the suggestion unit suggests supplements that can be easily taken. In this way, the suggestion unit can suggest nutrients according to the individual needs of the user and support health management and dieting. Furthermore, the suggestion unit uses visual displays and explanatory text to present the suggested content to the user in an easy-to-understand manner. For example, images of foods and graphs of nutritional components are displayed so that the user can intuitively understand the suggested content. In this way, the suggestion unit makes it easier for the user to put the suggested content into practice, thereby improving the effectiveness of health management and dieting.

[0069] The database unit manages a food database for suggesting specific food names. The food database includes, for example, information such as food names, nutritional components, and calories, but is not limited to such examples. For example, the database unit registers food names and their nutritional components in the food database. In addition, the database unit can also register calorie information in the food database. In addition, the database unit can also register images of foods in the food database. For example, the database unit registers food names and their nutritional components in the food database. Calorie information is registered. An image of a food is registered. Specifically, the database unit accurately manages nutritional component and calorie information of foods to enable a user to quickly provide information required by the user. For example, a database that details food names, nutritional components, and calorie information is constructed to make it easier for a user to search and filter. In addition, by registering images of foods, it is easier for a user to visually select foods. The database unit periodically updates the contents of the database and adds new foods and nutritional information. For example, when a new food appears on the market, nutritional component and calorie information of the food is quickly added to the database. In addition, when nutritional component and calorie information of an existing food is changed, the information is updated. This allows the database unit to always provide the latest information, enabling the user to select foods based on accurate information. Furthermore, the database unit also has a function of making individual suggestions taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, suggestions are made that exclude that food. Also, based on the user's preferences, preferred foods are preferentially suggested. This allows the database unit to make suggestions according to the individual needs of the user, supporting health management and dieting.

[0070] The tracking unit can track the user's meal history. The tracking unit, for example, records the type and amount of food consumed by the user. For example, the tracking unit can input the name and amount of food eaten by the user. The tracking unit can also have the user take a photo of the meal and record the meal history based on the photo. For example, the tracking unit inputs the name and amount of food eaten by the user. The user takes a photo of the meal and records the meal history based on the photo. The tracking unit can also record the seasonings and cooking methods used by the user. For example, the tracking unit inputs the seasonings and cooking methods used by the user. This allows the tracking unit to accurately grasp the user's meal history and reflect it in the next proposal. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the meal data input by the user to the generation AI and cause the generation AI to execute the recording of the meal history.

[0071] The plan unit can provide a diet plan. The plan unit can provide, for example, a diet plan according to the user's goal. For example, the plan unit can suggest a dietary restriction and an exercise program based on the weight loss goal set by the user. The plan unit can also provide an appropriate diet plan based on the user's health condition. For example, the plan unit can suggest a dietary restriction and an exercise program based on the user's health check data. Furthermore, the plan unit can adjust the diet plan based on the user's lifestyle. For example, the plan unit can provide a reasonable diet plan taking into account the user's lifestyle. As a result, the plan unit can support the user's health management by providing a diet plan according to the user's goal. Some or all of the above-mentioned processing in the plan unit can be performed, for example, using AI or without AI. For example, the plan unit can input the user's goal and health condition to the generation AI and cause the generation AI to execute the generation of the diet plan.

[0072] The collection unit can estimate the user's emotion and adjust the collection timing of the medical checkup data based on the emotion. For example, when the user is stressed, the collection unit collects the medical checkup data during a time period when the user can relax. For example, the collection unit performs a blood test during a time period when the user is relaxed. Furthermore, the collection unit can also collect detailed medical checkup data when the user is relaxed. For example, the collection unit performs a detailed blood test during a time period when the user is relaxed. Furthermore, the collection unit can also collect simple medical checkup data when the user is in a hurry. For example, the collection unit performs a simple blood test during a time period when the user is in a hurry. In this way, the collection unit can collect more appropriate data by adjusting the collection timing of the medical checkup data 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0073] The collection unit can analyze the user's past health checkup data and select the optimal collection method. The collection unit, for example, selects the most effective collection method from the user's past health checkup data. For example, the collection unit analyzes the user's past blood test results and selects the optimal collection method. The collection unit can also adjust the collection frequency based on the user's past health checkup data. For example, the collection unit analyzes the user's past health checkup data and adjusts the collection frequency. Furthermore, the collection unit can analyze the user's past health checkup data and select a collection method that focuses on a specific health index. For example, the collection unit analyzes the user's past health checkup data and selects a collection method that focuses on a specific health index. As a result, the collection unit can efficiently collect data by selecting the optimal collection method based on the past health checkup data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health checkup data to the generation AI and cause the generation AI to select the optimal collection method.

[0074] The collection unit can perform filtering based on the user's current living situation and health condition when collecting the health checkup data. The collection unit, for example, takes into account the user's current living situation and collects appropriate health checkup data. For example, when the user is busy, the collection unit collects simple health checkup data. The collection unit can also collect only necessary data based on the user's current health condition. For example, when the user is healthy, the collection unit collects basic health checkup data. Furthermore, the collection unit can filter the data to be collected by taking into account the user's lifestyle habits. For example, when the user is exercising, the collection unit collects data related to exercise. In this way, the collection unit can collect only necessary data by filtering the data according to the user's living situation and health condition. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and health condition to the generation AI and cause the generation AI to perform data filtering.

[0075] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the emotion. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related data. For example, when the user is feeling stressed, the collection unit preferentially collects stress hormone levels. In addition, the collection unit can also collect overall health data when the user is relaxed. For example, when the user is relaxed, the collection unit collects blood test and electrocardiogram data. In addition, when the user is in a hurry, the collection unit can preferentially collect only important health indicators. For example, when the user is in a hurry, the collection unit preferentially collects important data such as blood pressure and heart rate. In this way, the collection unit can preferentially collect important data by determining the priority of data 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0076] When collecting health checkup data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific area, the collection unit preferentially collects health data related to the area. For example, when the user is in a high altitude, the collection unit collects health data related to the high altitude. The collection unit can also collect data taking into account health risks specific to the area based on the geographical location information of the user. For example, when the user is in a tropical area, the collection unit collects data related to tropical diseases. Furthermore, when the user is traveling, the collection unit can also preferentially collect data related to health risks at the travel destination. For example, when the user is traveling, the collection unit collects data related to health risks at the travel destination. In this way, the collection unit can collect data related to health risks specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0077] The collection unit can analyze the user's social media activity and collect related data when collecting health checkup data. For example, the collection unit analyzes health-related posts from the user's social media activity and collects related data. For example, the collection unit collects health-related information posted by the user on social media. The collection unit can also identify health-related concerns based on the user's social media activity and collect related data. For example, the collection unit collects data related to health topics in which the user has shown interest on social media. Furthermore, the collection unit can analyze the user's social media activity and collect data related to health risks. For example, the collection unit collects information related to health risks shared by the user on social media. In this way, the collection unit can collect data related to health-related concerns and risks by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0078] The analysis unit can estimate the user's emotion and adjust the way of expressing the analysis based on the emotion. For example, when the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, when the user is stressed, the analysis unit visually displays the analysis result using a graph or chart. In addition, the analysis unit can provide a detailed analysis result when the user is relaxed. For example, when the user is relaxed, the analysis unit provides a detailed text report. In addition, when the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. For example, when the user is in a hurry, the analysis unit provides a short report that summarizes the main points. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the way of expressing the analysis 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup data during the analysis. The analysis unit performs detailed analysis on important health indicators, for example. For example, the analysis unit performs detailed analysis on blood test data. The analysis unit can also perform simple analysis on general health indicators. For example, the analysis unit performs simple analysis on weight and height. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health condition. For example, the analysis unit performs simple analysis when the user is healthy, and performs detailed analysis when the user has a health risk. Thereby, the analysis unit can perform analysis efficiently by adjusting the level of detail of the analysis according to the importance of the health checkup data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the health checkup data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the medical examination data during analysis. For example, the analysis unit applies a specific analysis algorithm to blood test data. For example, the analysis unit applies regression analysis to the blood test data. The analysis unit can also apply another analysis algorithm to electrocardiogram data. For example, the analysis unit applies clustering to the electrocardiogram data. Furthermore, the analysis unit can also apply an image analysis algorithm to image diagnostic data. For example, the analysis unit applies deep learning to the image diagnostic data. As a result, the analysis unit applies an appropriate analysis algorithm depending on the category of the medical examination data, thereby improving the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the medical examination data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the emotion. For example, when the user is stressed, the analysis unit provides a short and to the point analysis result. For example, when the user is stressed, the analysis unit provides a short report summarizing the main points. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user is relaxed, the analysis unit provides a detailed text report. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. For example, when the user is in a hurry, the analysis unit provides a short report summarizing the main points. Thereby, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] The analysis unit can determine the priority of the analysis based on the submission time of the health checkup data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest health checkup data. For example, the analysis unit prioritizes the analysis of the latest blood test data. The analysis unit can also postpone data submitted earlier. For example, the analysis unit postpones blood test data submitted earlier. Furthermore, the analysis unit can adjust the priority of the analysis based on the submission time. For example, the analysis unit adjusts the priority of the analysis based on the submission time. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the submission time of the health checkup data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time of the health checkup data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the medical examination data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant blood test data. The analysis unit can also postpone data with low relevance. For example, the analysis unit postpones blood test data with low relevance. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the medical examination data. For example, the analysis unit adjusts the order of analysis based on the relevance of the medical examination data. As a result, the analysis unit can prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of the medical examination data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the medical examination data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the emotion. For example, when the user is stressed, the suggestion unit makes a simple and easy-to-understand proposal. For example, when the user is stressed, the suggestion unit makes a proposal in concise text. In addition, the suggestion unit can also make a detailed proposal when the user is relaxed. For example, when the user is relaxed, the suggestion unit makes a proposal using detailed text or graphs. Furthermore, when the user is in a hurry, the suggestion unit can also make a proposal that focuses on the main points. For example, when the user is in a hurry, the suggestion unit makes a short proposal that summarizes the main points. In this way, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the way of expressing the proposal 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0085] The suggestion unit can adjust the level of detail of the proposal based on the importance of the nutrient at the time of suggestion. For example, the suggestion unit makes a detailed proposal for an important nutrient. For example, the suggestion unit makes a detailed proposal for vitamin C. The suggestion unit can also make a simple proposal for a common nutrient. For example, the suggestion unit makes a simple proposal for a common vitamin. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the health condition of the user. For example, the suggestion unit makes a simple proposal when the user is healthy, and makes a detailed proposal when the user has a health risk. Thereby, the suggestion unit can make an efficient proposal by adjusting the level of detail of the proposal according to the importance of the nutrient. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the nutrient to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of nutrients when making a suggestion. For example, the suggestion unit applies a specific suggestion algorithm to vitamins. For example, the suggestion unit applies a recommendation system to vitamins. The suggestion unit can also apply another suggestion algorithm to minerals. For example, the suggestion unit applies a rule-based suggestion to minerals. Furthermore, the suggestion unit can apply a different suggestion algorithm to proteins. For example, the suggestion unit applies a machine learning algorithm to proteins. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the category of nutrients, thereby improving the accuracy of the suggestion. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of nutrients to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the emotion. For example, when the user is stressed, the suggestion unit makes a short and to the point proposal. For example, when the user is stressed, the suggestion unit makes a short proposal that summarizes the main points. In addition, the suggestion unit can also make a detailed proposal when the user is relaxed. For example, when the user is relaxed, the suggestion unit makes a proposal using detailed text or graphs. In addition, the suggestion unit can also make a concise proposal when the user is in a hurry. For example, when the user is in a hurry, the suggestion unit makes a short proposal that summarizes the main points. Thereby, the suggestion unit can provide a proposal of an appropriate length for the user by adjusting the length of the proposal 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0088] The suggestion unit can determine the priority of the suggestion based on the submission time of the nutrient at the time of suggestion. For example, the suggestion unit preferentially suggests the latest nutrient data. For example, the suggestion unit preferentially suggests the latest vitamin data. The suggestion unit can also postpone data submitted earlier. For example, the suggestion unit postpones vitamin data submitted earlier. Furthermore, the suggestion unit can adjust the priority of the suggestion based on the submission time. For example, the suggestion unit adjusts the priority of the suggestion based on the submission time. Thereby, the suggestion unit can preferentially suggest the latest nutrient data by determining the priority of the suggestion based on the submission time of the nutrient. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the submission time of the nutrient to the generation AI and cause the generation AI to execute the determination of the priority of the suggestion.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of nutrients at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant nutrients. For example, the suggestion unit preferentially suggests highly relevant vitamins. The suggestion unit can also postpone less relevant nutrients. For example, the suggestion unit postpones less relevant vitamins. Furthermore, the suggestion unit can also adjust the order of suggestions based on the relevance of nutrients. For example, the suggestion unit adjusts the order of suggestions based on the relevance of nutrients. As a result, the suggestion unit can preferentially suggest highly relevant nutrients by adjusting the order of suggestions based on the relevance of nutrients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of nutrients to the generation AI and cause the generation AI to adjust the order of suggestions.

[0090] The database unit can estimate the user's emotion and adjust the update frequency of the database based on the emotion. For example, when the user is stressed, the database unit reduces the update frequency of the database. For example, when the user is stressed, the database unit updates the database once a week. Also, when the user is relaxed, the database unit can increase the update frequency of the database. For example, when the user is relaxed, the database unit updates the database every day. Furthermore, when the user is in a hurry, the database unit can also update only necessary data preferentially. For example, when the user is in a hurry, the database unit updates only necessary data preferentially. In this way, the database unit can update the database at an optimal frequency for the user by adjusting the update frequency of the database 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. A part or all of the above-mentioned processing in the database unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the database unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] The database unit can adjust the level of detail of data based on the importance of the nutrient data of food when managing the database. For example, the database unit manages detailed data for important nutrient data. For example, the database unit manages detailed data for vitamin C. The database unit can also manage simple data for general nutrient data. For example, the database unit manages simple data for general vitamins. Furthermore, the database unit can adjust the level of detail of data according to the health condition of the user. For example, the database unit manages simple data when the user is healthy, and manages detailed data when the user has a health risk. This allows the database unit to efficiently manage data by adjusting the level of detail of data according to the importance of the nutrient data of food. Part or all of the above-mentioned processing in the database unit may be performed, for example, using AI, or may be performed without using AI. For example, the database unit can input the importance of nutrient data to the generation AI and cause the generation AI to adjust the level of detail of the data.

[0092] The database unit can apply different data management algorithms according to the food category when managing the database. For example, the database unit applies a specific data management algorithm to vegetables. For example, the database unit applies an index creation algorithm to vegetables. The database unit can also apply a different data management algorithm to meat. For example, the database unit applies a data compression algorithm to meat. Furthermore, the database unit can apply a different data management algorithm to seafood. For example, the database unit applies a data cleansing algorithm to seafood. As a result, the database unit applies an appropriate data management algorithm according to the food category, thereby improving the accuracy of data management. Part or all of the above-mentioned processing in the database unit may be performed, for example, using AI, or may be performed without using AI. For example, the database unit can input the food category to the generation AI and cause the generation AI to apply the data management algorithm.

[0093] The database unit can estimate the user's emotion and adjust the display method of the database based on the emotion. For example, when the user is stressed, the database unit provides a simple and highly visible display method. For example, when the user is stressed, the database unit displays data in concise text. In addition, when the user is relaxed, the database unit can also provide a display method including detailed information. For example, when the user is relaxed, the database unit displays data using detailed text or graphs. In addition, when the user is in a hurry, the database unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the database unit provides a short display method that summarizes the main points. In this way, the database unit can provide a display method that is highly visible to the user by adjusting the display method of the database 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. A part or all of the above-mentioned processing in the database unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the database unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] The database unit can determine the priority of data based on the submission time of food when managing the database. For example, the database unit preferentially manages the latest food data. For example, the database unit preferentially manages the latest vegetable data. The database unit can also postpone data submitted earlier. For example, the database unit postpones vegetable data submitted earlier. Furthermore, the database unit can also adjust the priority of data based on the submission time. For example, the database unit adjusts the priority of data based on the submission time. Thereby, the database unit can preferentially manage the latest food data by determining the priority of data based on the submission time of food. Part or all of the above-mentioned processing in the database unit may be performed using, for example, AI, or may be performed without using AI. For example, the database unit can input the submission time of food to the generation AI and cause the generation AI to execute the determination of the priority of data.

[0095] The database unit can adjust the order of data based on the relevance of food when managing the database. For example, the database unit preferentially manages highly relevant food data. For example, the database unit preferentially manages highly relevant vegetable data. The database unit can also postpone low-relevance food data. For example, the database unit postpones low-relevance vegetable data. Furthermore, the database unit can also adjust the order of data based on the relevance of food data. For example, the database unit adjusts the order of data based on the relevance of food data. In this way, the database unit can preferentially manage highly relevant food data by adjusting the order of data based on the relevance of food. A part or all of the above-mentioned processing in the database unit may be performed, for example, using AI, or may be performed without using AI. For example, the database unit can input the relevance of food to the generation AI and cause the generation AI to adjust the order of data.

[0096] The tracking unit can estimate the user's emotion and adjust the meal history tracking method based on the emotion. For example, the tracking unit provides a simple tracking method when the user is stressed. For example, the tracking unit provides a simple input form when the user is stressed. The tracking unit can also provide a detailed tracking method when the user is relaxed. For example, the tracking unit provides a detailed input form or a photo upload function when the user is relaxed. Furthermore, the tracking unit can also provide a tracking method that focuses on the main points when the user is in a hurry. For example, the tracking unit provides a simple input form when the user is in a hurry. Thereby, the tracking unit can provide a tracking method appropriate for the user by adjusting the meal history tracking 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. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0097] The tracking unit can select the optimal tracking method by analyzing the user's past meal history during tracking. The tracking unit, for example, selects the most effective tracking method from the user's past meal history. For example, the tracking unit analyzes the user's past meal history and selects the optimal tracking method. The tracking unit can also adjust the tracking frequency based on the user's past meal history. For example, the tracking unit analyzes the user's past meal history and adjusts the tracking frequency. Furthermore, the tracking unit can analyze the user's past meal history and select a tracking method that emphasizes a specific nutrient. For example, the tracking unit analyzes the user's past meal history and selects a tracking method that emphasizes a specific nutrient. As a result, the tracking unit can efficiently track the meal history by selecting the optimal tracking method based on the user's past meal history. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past meal history to the generation AI and cause the generation AI to select the optimal tracking method.

[0098] The tracking unit can estimate the user's emotions and determine the priority of the meal history to be tracked based on the emotions. For example, when the user is feeling stressed, the tracking unit preferentially tracks stress-related meal history. For example, when the user is feeling stressed, the tracking unit preferentially tracks the history of foods that are useful for stress reduction. In addition, the tracking unit can also track the overall meal history when the user is relaxed. For example, when the user is relaxed, the tracking unit tracks the meal history taking into account the overall nutritional balance. Furthermore, when the user is in a hurry, the tracking unit can also preferentially track only important meal history. For example, when the user is in a hurry, the tracking unit preferentially tracks the intake history of major nutrients. Thereby, the tracking unit can preferentially track important meal history by determining the priority of the meal history according to the user's emotions. 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. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0099] The tracking unit can prioritize tracking of highly relevant history in consideration of the geographical location information of the user during tracking. For example, when the user is in a specific area, the tracking unit prioritizes tracking of a meal history related to the area. For example, when the user is in a high altitude, the tracking unit tracks a meal history related to the high altitude. The tracking unit can also track a history taking into account a region-specific dietary risk based on the geographical location information of the user. For example, when the user is in a tropical area, the tracking unit tracks a dietary history related to a tropical disease. Furthermore, when the user is traveling, the tracking unit can also prioritize tracking of a history related to a dietary risk at a travel destination. For example, when the user is traveling, the tracking unit tracks a history related to a dietary risk at a travel destination. In this way, the tracking unit can track a history related to a region-specific dietary risk by considering the geographical location information of the user. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute tracking of a highly relevant history.

[0100] The plan unit can estimate the user's emotions and adjust the method of providing the diet plan based on the emotions. For example, when the user is stressed, the plan unit provides a simple and easy-to-understand diet plan. For example, when the user is stressed, the plan unit provides a diet plan in concise text. In addition, the plan unit can also provide a detailed diet plan when the user is relaxed. For example, when the user is relaxed, the plan unit provides a diet plan using detailed text and graphs. Furthermore, when the user is in a hurry, the plan unit can also provide a diet plan that focuses on the main points. For example, when the user is in a hurry, the plan unit provides a short diet plan that summarizes the main points. In this way, the plan unit can provide a plan that is easy for the user to understand by adjusting the method of providing the diet plan according to the user's emotions. 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. A part or all of the above-mentioned processing in the plan unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] When providing a plan, the plan unit can provide an optimal plan by referring to the user's past diet history. The plan unit, for example, provides the most effective plan from the user's past diet history. For example, the plan unit analyzes the user's past diet history and provides an optimal plan. The plan unit can also adjust the level of detail of the plan based on the user's past diet history. For example, the plan unit analyzes the user's past diet history and adjusts the level of detail of the plan. Furthermore, the plan unit can analyze the user's past diet history and provide a plan that focuses on a specific goal. For example, the plan unit analyzes the user's past diet history and provides a plan that focuses on a specific goal. As a result, the plan unit can efficiently support dieting by providing an optimal plan based on the user's past diet history. Part or all of the above-mentioned processing in the plan unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan unit can input the user's past diet history into the generation AI and cause the generation AI to provide an optimal plan.

[0102] The plan unit can estimate the user's emotions and determine the priority of diet plans based on the emotions. For example, when the user is stressed, the plan unit provides a plan aimed at stress reduction with priority. For example, when the user is stressed, the plan unit provides a meal plan with a relaxing effect. In addition, when the user is relaxed, the plan unit can also provide a plan aimed at improving overall health. For example, when the user is relaxed, the plan unit provides a balanced meal plan. Furthermore, when the user is in a hurry, the plan unit can also provide a plan that will produce results in a short period of time with priority. For example, when the user is in a hurry, the plan unit provides a plan that is expected to reduce weight in a short period of time. In this way, the plan unit can provide an optimal plan for the user by determining the priority of diet plans according to the user's emotions. 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. A part or all of the above-mentioned processing in the plan unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0103] When providing a plan, the plan unit can provide an optimal plan by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the plan unit provides a diet plan related to the area. For example, when the user is at high altitude, the plan unit provides a meal plan suitable for high altitude. The plan unit can also provide a plan that takes into consideration region-specific dietary risks based on the user's geographical location information. For example, when the user is in a tropical area, the plan unit provides a meal plan that is useful for preventing tropical diseases. Furthermore, when the user is traveling, the plan unit can also provide a plan related to dietary risks at the travel destination. For example, when the user is traveling, the plan unit provides a plan corresponding to the dietary risks at the travel destination. In this way, the plan unit can provide a plan related to region-specific dietary risks by taking into consideration the geographical location information of the user. Part or all of the above-mentioned processing in the plan unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan unit can input the user's geographical location information to the generation AI and cause the generation AI to provide an optimal plan.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When analyzing the user's health checkup data, the analysis unit can adjust the analysis results by taking into account the user's lifestyle habit data. For example, the analysis unit evaluates health risks by taking into account the user's exercise habits and sleep patterns. The analysis unit can also evaluate nutritional balance by taking into account the user's eating patterns. Furthermore, the analysis unit can make suggestions for stress management by taking into account the user's stress level. In this way, the analysis unit can provide more accurate analysis results by taking into account the user's lifestyle habit data.

[0106] The suggestion unit can suggest an exercise plan that matches the user's lifestyle based on the user's health check data. For example, if the user lives a desk-based lifestyle, the suggestion unit can suggest simple exercises that can be done at the office. If the user prefers outdoor activities, the suggestion unit can also suggest exercise plans such as hiking and jogging. Furthermore, the suggestion unit can provide a plan that adjusts the exercise intensity and frequency in consideration of the user's exercise history. In this way, the suggestion unit can support health management by providing an exercise plan that matches the user's lifestyle.

[0107] When collecting the user's health checkup data, the collection unit can adjust the collection method taking into account the user's living environment data. For example, if the user lives in an urban area, the collection unit collects data related to city-specific health risks. Also, if the user lives in a rural area, the collection unit can collect data related to rural-specific health risks. Furthermore, the collection unit can adjust the collection frequency and collection items based on the user's living environment. In this way, the collection unit can collect more appropriate health checkup data by taking into account the user's living environment data.

[0108] When analyzing the user's health checkup data, the analysis unit can adjust the analysis results by taking into account the user's genetic information. For example, the analysis unit can evaluate the risk of a particular disease by taking into account the user's family history and genetic risk. The analysis unit can also provide personalized nutritional advice based on the user's genetic information. Furthermore, the analysis unit can adjust the exercise plan based on the user's genetic information. In this way, the analysis unit can provide more accurate analysis results by taking into account the user's genetic information.

[0109] The suggestion unit can suggest a health management plan that matches the user's hobbies and interests based on the user's health checkup data. For example, if the user's hobby is cooking, the suggestion unit can suggest healthy recipes. Also, if the user's hobby is gardening, the suggestion unit can suggest an exercise plan through gardening. Furthermore, the suggestion unit can suggest a stress management method based on the user's hobbies and interests. In this way, the suggestion unit can support the user's health management by providing a health management plan that matches the user's hobbies and interests.

[0110] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis result based on the emotions. For example, the analysis unit can emphasize positive feedback when the user is stressed. The analysis unit can also provide detailed feedback when the user is relaxed. Furthermore, the analysis unit can provide concise feedback that focuses on the main points when the user is in a hurry. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the feedback method according to the user's emotions.

[0111] The suggestion unit can estimate the user's emotions and prioritize the suggested contents based on the emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes suggestions that are helpful in reducing stress. In addition, when the user is relaxed, the suggestion unit can also make suggestions related to long-term health improvement. Furthermore, when the user is in a hurry, the suggestion unit can also prioritize suggestions that will have an effect in a short period of time. In this way, the suggestion unit can provide optimal suggestions for the user by prioritizing the suggested contents according to the user's emotions.

[0112] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed. The collection unit can also increase the frequency of data collection when the user is relaxed. Furthermore, the collection unit can collect only the minimum amount of data required when the user is in a hurry. In this way, the collection unit can adjust the frequency of data collection according to the user's emotions, thereby realizing data collection that places less of a burden on the user.

[0113] The tracking unit can estimate the user's emotions and adjust the meal history tracking method based on the emotions. For example, the tracking unit can provide a simple tracking method when the user is stressed. The tracking unit can also provide a detailed tracking method when the user is relaxed. Furthermore, the tracking unit can provide a tracking method that focuses on the main points when the user is in a hurry. In this way, the tracking unit can provide a tracking method appropriate for the user by adjusting the meal history tracking method according to the user's emotions.

[0114] The plan unit can estimate the user's emotions and adjust the method of providing the diet plan based on the emotions. For example, when the user is feeling stressed, the plan unit can provide a simple and easy-to-understand diet plan. Also, when the user is relaxed, the plan unit can provide a detailed diet plan. Furthermore, when the user is in a hurry, the plan unit can provide a diet plan that focuses on the main points. In this way, the plan unit can provide a plan that is easy for the user to understand by adjusting the method of providing the diet plan according to the user's emotions.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The collection unit collects the user's health checkup data. The user's health checkup data includes blood test results, weight, height, blood pressure, etc. The collection unit acquires data from the electronic medical record system, as well as data entered by the user and data from the wearable device. For example, blood test results are acquired from the electronic medical record system, weight and height are entered by the user, and data such as heart rate and number of steps are collected from the wearable device. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit evaluates the user's health condition using statistical analysis, predicts health risks using machine learning algorithms, and extracts patterns related to the health condition using data mining techniques. Step 3: The suggestion unit suggests necessary nutrients based on the analysis results obtained by the analysis unit. The suggestions are based on nutrients such as vitamins, minerals, and proteins. For example, the suggestion unit suggests intake of vitamin C based on the user's health condition, suggests intake of minerals based on the nutritional condition, and suggests intake of protein based on the goal. Step 4: The database unit manages a food database for suggesting specific food names. The food database includes information such as food names, nutritional components, and calories. For example, food names, their nutritional components, calorie information, and food images are registered.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, database unit, tracking unit, and plan unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects the user's health checkup data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes necessary nutrients based on the analysis results. The database unit is realized, for example, by the database 24 of the data processing device 12 and manages specific food names. The tracking unit is realized, for example, by the control unit 46A of the smart device 14 and tracks the user's meal history. The plan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a diet plan according to the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, database unit, tracking unit, and plan unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects the user's health checkup data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests necessary nutrients based on the analysis results. The database unit is realized, for example, by the database 24 of the data processing device 12 and manages specific food names. The tracking unit is realized, for example, by the control unit 46A of the smart glasses 214 and tracks the user's meal history. The plan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a diet plan according to the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, database unit, tracking unit, and plan unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects the user's health checkup data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes necessary nutrients based on the analysis results. The database unit is realized, for example, by the database 24 of the data processing device 12 and manages specific food names. The tracking unit is realized, for example, by the control unit 46A of the headset type terminal 314 and tracks the user's meal history. The plan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a diet plan according to the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, database unit, tracking unit, and plan unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects the user's health checkup data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes necessary nutrients based on the analysis results. The database unit is realized, for example, by the database 24 of the data processing device 12 and manages specific food names. The tracking unit is realized, for example, by the control unit 46A of the robot 414 and tracks the user's meal history. The plan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a diet plan according to the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] These emotions are distributed in the 3 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.

[0173] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Appendix 1) A collection unit that collects health checkup data of a user; an analysis unit that analyzes the data collected by the collection unit; A suggestion unit that suggests necessary nutrients based on the analysis result obtained by the analysis unit; Equipped with a database unit that manages a food database to suggest specific food names A system characterized by: (Appendix 2) Equipped with a tracking unit that tracks the user's meal history 2. The system of claim 1, (Appendix 3) Equipped with a planning department that provides diet plans 2. The system of claim 1, (Appendix 4) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting health checkup data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the user's past health checkup data and select the appropriate collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting health checkup data, filtering is performed based on the user's current living situation and health condition. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting health checkup data, prioritize collection of relevant data based on the user's geographic location information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting health checkup data, analyze users' social media activity and collect related data. 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, adjust the level of detail based on the importance of the medical examination data 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of medical examination data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the analysis priority will be determined based on the timing of submission of health examination data. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the medical examination data 2. The system of claim 1, (Appendix 16) 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 17) 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 18) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the nutrient category. 2. The system of claim 1, (Appendix 19) 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 20) The suggestion unit, At the time of submission, proposals will be prioritized based on when nutrients were submitted. 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, adjust the order of suggestions based on nutrient relevance 2. The system of claim 1, (Appendix 22) The database unit includes: Infer user emotions and adjust the frequency of database updates based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The database unit includes: When managing the database, adjust the level of detail for food nutrition data based on its importance 2. The system of claim 1, (Appendix 24) The database unit includes: When managing the database, different data management algorithms are applied depending on the food category. 2. The system of claim 1, (Appendix 25) The database unit includes: Inferring user emotions and adjusting the way the database is displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The database unit includes: When managing the database, prioritize data based on when foods were submitted 2. The system of claim 1, (Appendix 27) The database unit includes: When maintaining the database, order the data based on food relevance 2. The system of claim 1, (Appendix 28) The tracking unit is Inferring a user's emotions and adjusting the method of tracking food history based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The tracking unit is When tracking, the system analyzes the user's past meal history to select the optimal tracking method. 2. The system of claim 1, (Appendix 30) The tracking unit is Estimate the user's emotions and prioritize the meal history to be tracked based on the estimated user's emotions. 2. The system of claim 1, (Appendix 31) The tracking unit is When tracking, the geographic location of the user is taken into account to prioritize the most relevant history. 2. The system of claim 1, (Appendix 32) The plan section includes: To estimate a user's emotion and adjust a method of providing a diet plan based on the estimated user's emotion. 2. The system of claim 1, (Appendix 33) The plan section includes: When providing a plan, the system refers to the user's past diet history to provide the optimal plan. 2. The system of claim 1, (Appendix 34) The plan section includes: Inferring a user's emotions and prioritizing diet plans based on the estimated user's emotions 2. The system of claim 1, (Appendix 35) The plan section includes: When offering plans, the most suitable plan is provided taking into account the user's geographic location information. 2. The system of claim 1, [Explanation of symbols]

[0189] 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 collection unit that collects health checkup data of a user; an analysis unit that analyzes the data collected by the collection unit; A suggestion unit that suggests necessary nutrients based on the analysis result obtained by the analysis unit; Equipped with a database unit that manages a food database to suggest specific food names A system characterized by:

2. Equipped with a tracking unit that tracks the user's meal history 2. The system of claim 1 .

3. Equipped with a planning department that provides diet plans 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting health checkup data based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the user's past health checkup data and select the appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: When collecting health checkup data, filtering is performed based on the user's current living situation and health condition.

2. The system of claim 1 .

7. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

8. The collecting unit includes: When collecting health checkup data, prioritize collection of data that is more relevant based on the user's geographic location information 2. The system of claim 1 .

9. The collecting unit includes: When collecting health checkup data, analyze users' social media activity and collect related data.

2. The system of claim 1 .

Images