System

Abstract

To provide a system for proposing specific health management on the basis of data on a user's body.SOLUTION: A system comprises a reception part, an analysis part, a recommendation part, and a specifying part. The reception part inputs data on a user's body. The analysis part analyzes the data inputted by the reception part to be collated with other persons' data. The recommendation part recommends diet and exercise based on specific criteria for the user on the basis of the analysis result obtained by the analysis part. The specifying part specifies the user's sickness risk on the basis of the analysis result obtained by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide specific health management suggestions based on data about the body of an individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to provide specific health management suggestions based on data relating to the user's body. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and an identification unit. The reception unit inputs data related to the user's body. The analysis unit analyzes the data input by the reception unit and compares it with data of others. The recommendation unit recommends a diet and exercise based on specific criteria to the user based on the analysis results obtained by the analysis unit. The identification unit identifies the user's risk of disease based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide specific health management suggestions based on data related to the user's body. [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) A health management system according to an embodiment of the present invention is a system that inputs data about a user's body, compares it with data from others, analyzes it, and recommends appropriate diet and exercise, and identifies disease risk. In this health management system, a user inputs information such as height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. Next, a reception unit receives these data, and an analysis unit analyzes the data using AI and compares it with data from others. Based on the analysis results, a recommendation unit recommends appropriate diet and exercise to the user, and an identification unit identifies the user's disease risk. Furthermore, a provision unit provides the user with information indicating the disease risk identified by the identification unit, and proposes a menu and a method for improving lifestyle habits for disease prevention. For example, a user inputs information such as height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. Next, a reception unit receives these data, and an analysis unit analyzes the data using AI and compares it with data from others. Based on the analysis results, the recommendation unit recommends appropriate diet and exercise to the user, and the identification unit identifies the user's disease risk. Furthermore, the provision unit provides the user with information indicating the disease risk identified by the identification unit, and suggests menus and lifestyle improvement methods for disease prevention. In this way, the health management system can grasp the user's health condition and help maintain health by encouraging appropriate diet and exercise.

[0029] The health management system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and an identification unit. The reception unit inputs data related to the user's body. The data related to the user's body includes, but is not limited to, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. The reception unit, for example, receives data input by the user and stores it in a database. The analysis unit uses AI to analyze the data input by the reception unit and compares it with other people's data. The analysis unit, for example, uses a machine learning algorithm to analyze the user's data and compare it with other people's data. The recommendation unit recommends appropriate diet and exercise to the user based on the analysis result obtained by the analysis unit. The recommendation unit provides, for example, diet recipes and muscle training menus. The identification unit identifies the user's disease risk based on the analysis result obtained by the analysis unit. The identification unit identifies, for example, the disease risk of cerebral infarction, diabetes, myocardial infarction, aortic dissection, etc. As a result, the health management system according to the embodiment can analyze data relating to the user's body, recommend appropriate diet and exercise, and identify disease risks, thereby helping to maintain health.

[0030] The reception unit inputs data related to the user's body. The data related to the user's body includes, but is not limited to, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, medical history, etc. The reception unit, for example, accepts data input by the user and stores it in a database. Specifically, the user can manually input these data through a dedicated application or web interface, or automatically synchronize data from a smart device or wearable device. For example, a smart watch or fitness tracker can automatically collect data such as daily exercise amount, heart rate, and sleep pattern, and send it to the reception unit. In addition, medical data such as blood test results and medical history can also be obtained directly from a medical institution. This allows the user to reflect the latest health data in the system without any hassle. Furthermore, the reception unit performs error checks and data consistency checks when inputting data, so that accurate data is saved. For example, if an abnormal value is detected when inputting height or weight, a message is displayed to prompt the user to recheck. In addition, data is saved using encryption technology to protect the user's privacy. This enables the reception unit to efficiently and safely collect and manage various health data of users, thereby improving the reliability of the entire system.

[0031] The analysis unit uses AI to analyze the data input by the reception unit and compare it with the data of others. The analysis unit uses, for example, a machine learning algorithm to analyze the user's data and compare it with the data of others. Specifically, the analysis unit uses deep learning and clustering algorithms to extract patterns of the user's health condition and lifestyle habits and compares them with the data of other users who have similar patterns. For example, by analyzing the user's diet history and exercise history and comparing them with the data of other users who have similar dietary and exercise habits, it is possible to find effective diet methods and exercise plans. The analysis unit also analyzes time-series data to track changes in the user's health condition. For example, it is possible to analyze fluctuations in weight and body fat percentage over the past few months and predict the current health condition. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to analyze the user's health condition from multiple angles and provide appropriate advice according to individual needs.

[0032] The recommendation unit recommends appropriate diet and exercise to the user based on the analysis results obtained by the analysis unit. The recommendation unit provides, for example, diet recipes and muscle training menus. Specifically, it proposes customized meal plans and exercise programs according to the user's current health condition and goals. For example, it provides low-calorie, high-protein meal recipes to a user who wants to reduce body fat percentage, and proposes muscle training menus targeting specific muscle groups to a user who wants to increase muscle mass. In addition, the recommendation unit monitors daily progress based on the user's diet history and exercise history, and adjusts the recommendation content as necessary. For example, if the user is approaching the target weight, it advises the user to review the meal plan and move to the maintenance phase. In addition, the recommendation unit can make individually customized recommendations taking into account the user's preferences and allergy information. This allows the user to continue living a healthy life without straining themselves. In addition, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation content. For example, if a user actually tries a recipe provided, the results can be fed back and reflected in the next recommendation. This allows the recommendation unit to provide the user with an optimal diet and exercise plan, supporting them in maintaining their health.

[0033] The identification unit identifies the user's disease risk based on the analysis results obtained by the analysis unit. The identification unit identifies the disease risk, such as cerebral infarction, diabetes, myocardial infarction, and aortic dissection. Specifically, the identification unit uses a machine learning model to evaluate the disease risk based on the user's blood test results, medical history, and lifestyle data. For example, the identification unit analyzes data such as blood glucose level, cholesterol level, and blood pressure to predict the risk of diabetes and cardiovascular disease. The identification unit can also evaluate genetic risk factors by taking into account the user's family history and genetic information. This allows the user to understand his or her health condition more deeply and take preventive measures early. Furthermore, the identification unit presents the results of the risk assessment to the user in an easy-to-understand manner and suggests specific measures. For example, for a user with a high risk of diabetes, the identification unit advises the user on the importance of reviewing their diet, recommending exercise, and regular health checks. The identification unit also continuously monitors changes in the user's health condition and regularly updates the risk assessment. This allows the user to always take appropriate measures based on the latest health information. The identification unit can greatly contribute to maintaining health and preventing disease by identifying the user's health risks early and suggesting appropriate preventive measures.

[0034] The identification unit includes a provision unit that provides the user with information indicating the identified disease risk. The provision unit provides the user with information indicating the disease risk identified by the identification unit. The provision unit provides the user with information such as, for example, a risk value and preventive measures. In this way, the identified disease risk is provided to the user, allowing the user to take appropriate measures. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can generate information indicating the disease risk identified by the identification unit using an AI model and provide the information to the user.

[0035] The reception unit inputs data such as height, weight, body fat percentage, BMI, whole body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. The reception unit, for example, accepts data such as height, weight, body fat percentage, BMI, whole body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history input by the user. These data may be obtained, for example, using a measuring device, or may be self-reported by the user. The reception unit stores these data in a database and makes them available to the analysis unit. This allows for more accurate analysis by inputting detailed data about the user's body. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may analyze the data input by the user using an AI model to confirm the accuracy of the data.

[0036] The recommendation unit provides a diet recipe or muscle training menu. The recommendation unit provides a diet recipe or muscle training menu based on, for example, data related to the user's body. The diet recipe includes, for example, a menu that takes into account calorie restriction and nutritional balance. The muscle training menu includes, for example, the type of exercise, the number of times, and the intensity. This supports health maintenance by providing a specific diet recipe or muscle training menu to the user. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's data into an AI model to generate an optimal diet recipe or muscle training menu.

[0037] The identification unit identifies the disease risks of cerebral infarction, diabetes, myocardial infarction, and aortic dissection. The identification unit identifies the disease risks of cerebral infarction, diabetes, myocardial infarction, and aortic dissection, for example, based on data related to the user's body. For example, risk factors and diagnostic criteria are used to identify these disease risks. This makes it possible to take early measures by specifically identifying the user's disease risk. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's data into an AI model to identify the disease risk.

[0038] The providing unit proposes a menu and a method for improving lifestyle habits for disease prevention. The providing unit proposes a menu and a method for improving lifestyle habits for disease prevention, for example, based on the disease risk identified by the identifying unit. The menu includes, for example, nutritional balance and selection of ingredients. The method for improving lifestyle habits includes, for example, exercise habits and sleep habits. This supports the user's health maintenance by making specific suggestions for disease prevention. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's data into an AI model to generate an optimal menu and a method for improving lifestyle habits.

[0039] The reception unit can analyze the user's past data input history and select the optimal input method. The reception unit, for example, analyzes the user's past data input history and selects the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The reception unit can also analyze the user's past input history to determine a tendency to input data during a specific time period and prompt the user to input data during that time period. Furthermore, the reception unit can automatically complement data previously input by the user and simplify the input work. This makes it possible to provide the user with an optimal input method by analyzing the past data input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history into an AI model to generate an optimal input method.

[0040] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when inputting data. For example, when inputting the user's current health condition, the reception unit filters out abnormal values ​​by checking against past health data. The reception unit can also preferentially input highly relevant data based on the user's lifestyle habits (e.g., diet, exercise, etc.). Furthermore, if the user has set a specific health goal, the reception unit can also preferentially input data related to the goal. This makes it possible to collect more relevant data by filtering data based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current health condition and lifestyle habits into an AI model and perform filtering.

[0041] The reception unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting data. For example, when the user lives in a specific area, the reception unit preferentially inputs data related to the health risks of the area. In addition, when the user is traveling, the reception unit can also preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the reception unit can also preferentially input data related to the climate condition. In this way, data corresponding to the health risks specific to the area can be collected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model to generate highly relevant data.

[0042] The reception unit can analyze the user's social media activity and input related data at the time of data input. The reception unit automatically inputs related data based on, for example, diet and exercise information shared by the user on social media. The reception unit can also input related data from health-related accounts that the user follows on social media. Furthermore, the reception unit can also input related data from health-related groups that the user participates in on social media. In this way, data related to the user's lifestyle habits can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity into an AI model to generate related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance and provides the data to the user. The analysis unit can also perform a simplified analysis on data with low importance and provide the data to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data and perform the analysis starting from the important data. As a result, by adjusting the level of detail of the analysis according to the importance of the data, it is possible to perform a detailed analysis on important data. 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 importance of the data into an AI model and generate the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply an algorithm that analyzes nutritional balance to dietary data. The analysis unit can also apply an algorithm that analyzes calories burned to exercise data. Furthermore, the analysis unit can apply an algorithm that analyzes disease risk to health data. This allows for more accurate analysis results to be provided by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of data into an AI model to generate an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the time of data submission during the analysis. For example, the analysis unit preferentially analyzes the latest data and provides it to the user. The analysis unit can also perform a simplified analysis on old data and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the time of data submission and perform the analysis from the latest data. As a result, the analysis can be preferentially analyzed on the latest data by determining the priority of the analysis based on the time of data submission. 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 time of data submission into an AI model to generate the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit preferentially analyzes highly relevant data and provides it to the user. The analysis unit can also perform a simplified analysis on data with low relevance and provide it to the user. Furthermore, the analysis unit can determine the order of analysis according to the relevance of data and perform analysis starting from highly relevant data. As a result, by adjusting the order of analysis based on the relevance of data, highly relevant data can be preferentially analyzed. 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 relevance of data into an AI model and generate the order of analysis.

[0047] The recommendation unit can adjust the level of detail of the recommendation based on the user's health goal at the time of recommendation. For example, if the user aims to lose weight, the recommendation unit can provide a detailed recommendation regarding calorie restriction. In addition, if the user aims to increase muscle strength, the recommendation unit can also provide a detailed recommendation regarding a muscle training menu. Furthermore, if the user aims to maintain health, the recommendation unit can also provide a recommendation regarding a balanced diet and exercise. This allows the recommendation to be optimal for the user by adjusting the level of detail of the recommendation based on the user's health goal. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input the user's health goal into an AI model to generate the level of detail of the recommendation.

[0048] The recommendation unit can apply different recommendation algorithms according to the user's lifestyle at the time of recommendation. For example, if the user is busy, the recommendation unit recommends an effective exercise menu in a short time. In addition, if the user is leading a regular life, the recommendation unit can also recommend a balanced meal and exercise menu. Furthermore, if the user is leading an irregular life, the recommendation unit can also recommend an easy-to-follow meal and exercise menu. This makes it possible to provide more effective recommendations by applying an appropriate recommendation algorithm according to the user's lifestyle. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's lifestyle into an AI model and generate an appropriate recommendation algorithm.

[0049] The recommendation unit can determine the priority order of the recommendation based on the user's past recommendation history when making a recommendation. The recommendation unit provides an optimal recommendation based on, for example, recommendations made by the user in the past. The recommendation unit can also provide a recommendation that has been highly effective from the user's past recommendation history with priority. The recommendation unit can also analyze the user's past recommendation history and provide the most effective recommendation. This allows more effective recommendations to be provided by determining the priority order of the recommendation based on the user's past recommendation history. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history into an AI model to generate a priority order of the recommendation.

[0050] The recommendation unit can adjust the order of recommendations based on the user's related data when making recommendations. The recommendation unit can provide the most effective recommendations preferentially based on the user's health data, for example. The recommendation unit can also provide optimal meal recommendations based on the user's diet data. Furthermore, the recommendation unit can also provide optimal exercise recommendations based on the user's exercise data. This allows more effective recommendations to be provided by adjusting the order of recommendations based on the user's related data. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without using AI. For example, the recommendation unit can input the user's related data into an AI model to generate the order of recommendations.

[0051] The identification unit can select an optimal identification method by analyzing the user's past health data at the time of identification. The identification unit, for example, selects the most effective risk identification method based on the user's past health data. The identification unit can also preferentially identify a specific disease risk from the user's past health data. Furthermore, the identification unit can analyze the user's past health data and select the most relevant risk identification method. In this way, an optimal disease risk identification method can be provided by analyzing the user's past health data. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's past health data into an AI model to generate an optimal identification method.

[0052] The identification unit can customize the specific means based on the user's current health condition at the time of identification. For example, when the user inputs the current health condition, the identification unit filters out abnormal values ​​by checking against past health data. The identification unit can also preferentially input highly relevant data based on the user's lifestyle (eating, exercise, etc.). Furthermore, if the user has set a specific health goal, the identification unit can also preferentially input data related to the goal. This enables more accurate disease risk identification by customizing the specific means based on the user's current health condition. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's current health condition into an AI model to generate the specific means.

[0053] The identification unit can select the optimal identification method in consideration of the geographical location information of the user at the time of identification. For example, when the user lives in a specific area, the identification unit preferentially inputs data related to the health risks of the area. In addition, when the user is traveling, the identification unit can also preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the identification unit can also preferentially input data related to the climate condition. In this way, by considering the geographical location information of the user, it is possible to provide an identification method corresponding to the health risks specific to the area. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the geographical location information of the user into an AI model to generate an optimal identification method.

[0054] At the time of identification, the identification unit can analyze the social media activity of the user to identify the disease risk. The identification unit can identify the associated disease risk based on, for example, health information shared by the user on social media. The identification unit can also identify the associated disease risk from health-related accounts that the user follows on social media. Furthermore, the identification unit can also identify the associated disease risk from health-related groups that the user participates in on social media. In this way, the associated disease risk can be identified by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using AI or can be performed without using AI. For example, the identification unit can input the social media activity of the user into an AI model to identify the disease risk.

[0055] The providing unit can provide optimal information by referring to the user's past health data when providing information. The providing unit can provide the most effective information based on the user's past health data, for example. The providing unit can also provide information related to a specific disease risk preferentially from the user's past health data. Furthermore, the providing unit can analyze the user's past health data and provide the most relevant information. This makes it possible to provide optimal information by referring to the user's past health data. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's past health data into an AI model to generate optimal information.

[0056] The providing unit can provide optimal information by considering the device information of the user when providing information. For example, when the user is using a smartphone, the providing unit provides an information providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide an information providing method that is concise and highly visible. This makes it possible to provide information optimized for the device by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user into an AI model to generate optimal information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When analyzing the user's data, the analysis unit can adjust the analysis results based on the user's living environment. For example, if the user lives in an urban area, the analysis can take into account the health risks specific to cities (air pollution, stress, etc.). If the user lives in a rural area, the analysis can take into account the health risks specific to rural areas (use of pesticides, lack of exercise, etc.). Furthermore, if the user is engaged in a specific occupation, the analysis can take into account the health risks specific to that occupation (long working hours, physical labor, etc.). This makes it possible to identify health risks more accurately by adjusting the analysis results based on the user's living environment.

[0059] The recommendation unit can analyze the user's meal history and make recommendations to increase meal variety. For example, if the user frequently consumes a particular ingredient, a new recipe using that ingredient can be suggested. Also, if the user is lacking in a particular nutrient, a recipe containing an ingredient to supplement that nutrient can be suggested. Furthermore, if the user has a bias toward a particular eating pattern, a balanced meal menu can be suggested to improve that pattern. In this way, by increasing the variety of meals based on the user's meal history, improvement in nutritional balance can be expected.

[0060] The identification unit can analyze the user's exercise history and provide advice for maximizing the effect of exercise. For example, if the user has been performing a particular exercise for a long period of time, a new training method for maximizing the effect of the exercise can be suggested. In addition, if the user can obtain more effective results by changing the frequency or intensity of the exercise, a specific method can be suggested. Furthermore, if the user has lost motivation for a particular exercise, a new exercise program can be suggested to restore that motivation. In this way, advice for maximizing the effect of exercise based on the user's exercise history is provided to support health maintenance.

[0061] The providing unit can provide information corresponding to seasonal health risks based on the user's health data. For example, preventive measures against influenza and colds can be suggested in winter. Also, preventive measures against heat stroke and dehydration can be suggested in summer. Furthermore, measures against allergies can be suggested in spring and autumn. In this way, by providing information corresponding to seasonal health risks, the user can take appropriate measures.

[0062] The reception unit can have a function of evaluating the reliability of input data when the user inputs data. For example, the reception unit can detect abnormal values ​​by comparing the data self-reported by the user with past data. In addition, if the data entered by the user is likely to be inaccurate, the reception unit can urge the user to recheck the data. Furthermore, if the data entered by the user lacks consistency, the reception unit can provide advice on how to correct the data. In this way, by evaluating the reliability of the input data, more accurate data analysis is possible.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit inputs data related to the user's body. The data related to the user's body includes, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, medical history, etc. The reception unit accepts the data input by the user and stores it in a database. Step 2: The analysis unit uses AI to analyze the data entered by the reception unit and compare it with the data of others. The analysis unit uses a machine learning algorithm to analyze the user's data and compare it with the data of others. Step 3: The recommendation unit recommends appropriate diet and exercise to the user based on the analysis results obtained by the analysis unit. The recommendation unit provides diet recipes and muscle training menus. Step 4: The identification unit identifies the user's disease risk based on the analysis results obtained by the analysis unit. The identification unit identifies the disease risk of cerebral infarction, diabetes, myocardial infarction, aortic dissection, etc.

[0065] (Example 2) A health management system according to an embodiment of the present invention is a system that inputs data about a user's body, compares it with data from others, analyzes it, and recommends appropriate diet and exercise, and identifies disease risk. In this health management system, a user inputs information such as height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. Next, a reception unit receives these data, and an analysis unit analyzes the data using AI and compares it with data from others. Based on the analysis results, a recommendation unit recommends appropriate diet and exercise to the user, and an identification unit identifies the user's disease risk. Furthermore, a provision unit provides the user with information indicating the disease risk identified by the identification unit, and proposes a menu and a method for improving lifestyle habits for disease prevention. For example, a user inputs information such as height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. Next, a reception unit receives these data, and an analysis unit analyzes the data using AI and compares it with data from others. Based on the analysis results, the recommendation unit recommends appropriate diet and exercise to the user, and the identification unit identifies the user's disease risk. Furthermore, the provision unit provides the user with information indicating the disease risk identified by the identification unit, and suggests menus and lifestyle improvement methods for disease prevention. In this way, the health management system can grasp the user's health condition and help maintain health by encouraging appropriate diet and exercise.

[0066] The health management system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and an identification unit. The reception unit inputs data related to the user's body. The data related to the user's body includes, but is not limited to, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. The reception unit, for example, receives data input by the user and stores it in a database. The analysis unit uses AI to analyze the data input by the reception unit and compares it with other people's data. The analysis unit, for example, uses a machine learning algorithm to analyze the user's data and compare it with other people's data. The recommendation unit recommends appropriate diet and exercise to the user based on the analysis result obtained by the analysis unit. The recommendation unit provides, for example, diet recipes and muscle training menus. The identification unit identifies the user's disease risk based on the analysis result obtained by the analysis unit. The identification unit identifies, for example, the disease risk of cerebral infarction, diabetes, myocardial infarction, aortic dissection, etc. As a result, the health management system according to the embodiment can analyze data relating to the user's body, recommend appropriate diet and exercise, and identify disease risks, thereby helping to maintain health.

[0067] The reception unit inputs data related to the user's body. The data related to the user's body includes, but is not limited to, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, medical history, etc. The reception unit, for example, accepts data input by the user and stores it in a database. Specifically, the user can manually input these data through a dedicated application or web interface, or automatically synchronize data from a smart device or wearable device. For example, a smart watch or fitness tracker can automatically collect data such as daily exercise amount, heart rate, and sleep pattern, and send it to the reception unit. In addition, medical data such as blood test results and medical history can also be obtained directly from a medical institution. This allows the user to reflect the latest health data in the system without any hassle. Furthermore, the reception unit performs error checks and data consistency checks when inputting data, so that accurate data is saved. For example, if an abnormal value is detected when inputting height or weight, a message is displayed to prompt the user to recheck. In addition, data is saved using encryption technology to protect the user's privacy. This enables the reception unit to efficiently and safely collect and manage various health data of users, thereby improving the reliability of the entire system.

[0068] The analysis unit uses AI to analyze the data input by the reception unit and compare it with the data of others. The analysis unit uses, for example, a machine learning algorithm to analyze the user's data and compare it with the data of others. Specifically, the analysis unit uses deep learning and clustering algorithms to extract patterns of the user's health condition and lifestyle habits and compares them with the data of other users who have similar patterns. For example, by analyzing the user's diet history and exercise history and comparing them with the data of other users who have similar dietary and exercise habits, it is possible to find effective diet methods and exercise plans. The analysis unit also analyzes time-series data to track changes in the user's health condition. For example, it is possible to analyze fluctuations in weight and body fat percentage over the past few months and predict the current health condition. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to analyze the user's health condition from multiple angles and provide appropriate advice according to individual needs.

[0069] The recommendation unit recommends appropriate diet and exercise to the user based on the analysis results obtained by the analysis unit. The recommendation unit provides, for example, diet recipes and muscle training menus. Specifically, it proposes customized meal plans and exercise programs according to the user's current health condition and goals. For example, it provides low-calorie, high-protein meal recipes to a user who wants to reduce body fat percentage, and proposes muscle training menus targeting specific muscle groups to a user who wants to increase muscle mass. In addition, the recommendation unit monitors daily progress based on the user's diet history and exercise history, and adjusts the recommendation content as necessary. For example, if the user is approaching the target weight, it advises the user to review the meal plan and move to the maintenance phase. In addition, the recommendation unit can make individually customized recommendations taking into account the user's preferences and allergy information. This allows the user to continue living a healthy life without straining themselves. In addition, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation content. For example, if a user actually tries a recipe provided, the results can be fed back and reflected in the next recommendation. This allows the recommendation unit to provide the user with an optimal diet and exercise plan, supporting them in maintaining their health.

[0070] The identification unit identifies the user's disease risk based on the analysis results obtained by the analysis unit. The identification unit identifies the disease risk, such as cerebral infarction, diabetes, myocardial infarction, and aortic dissection. Specifically, the identification unit uses a machine learning model to evaluate the disease risk based on the user's blood test results, medical history, and lifestyle data. For example, the identification unit analyzes data such as blood glucose level, cholesterol level, and blood pressure to predict the risk of diabetes and cardiovascular disease. The identification unit can also evaluate genetic risk factors by taking into account the user's family history and genetic information. This allows the user to understand his or her health condition more deeply and take preventive measures early. Furthermore, the identification unit presents the results of the risk assessment to the user in an easy-to-understand manner and suggests specific measures. For example, for a user with a high risk of diabetes, the identification unit advises the user on the importance of reviewing their diet, recommending exercise, and regular health checks. The identification unit also continuously monitors changes in the user's health condition and regularly updates the risk assessment. This allows the user to always take appropriate measures based on the latest health information. The identification unit can greatly contribute to maintaining health and preventing disease by identifying the user's health risks early and suggesting appropriate preventive measures.

[0071] The identification unit includes a provision unit that provides the user with information indicating the identified disease risk. The provision unit provides the user with information indicating the disease risk identified by the identification unit. The provision unit provides the user with information such as, for example, a risk value and preventive measures. In this way, the identified disease risk is provided to the user, allowing the user to take appropriate measures. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can generate information indicating the disease risk identified by the identification unit using an AI model and provide the information to the user.

[0072] The reception unit inputs data such as height, weight, body fat percentage, BMI, whole body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. The reception unit, for example, accepts data such as height, weight, body fat percentage, BMI, whole body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history input by the user. These data may be obtained, for example, using a measuring device, or may be self-reported by the user. The reception unit stores these data in a database and makes them available to the analysis unit. This allows for more accurate analysis by inputting detailed data about the user's body. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may analyze the data input by the user using an AI model to confirm the accuracy of the data.

[0073] The recommendation unit provides a diet recipe or muscle training menu. The recommendation unit provides a diet recipe or muscle training menu based on, for example, data related to the user's body. The diet recipe includes, for example, a menu that takes into account calorie restriction and nutritional balance. The muscle training menu includes, for example, the type of exercise, the number of times, and the intensity. This supports health maintenance by providing a specific diet recipe or muscle training menu to the user. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's data into an AI model to generate an optimal diet recipe or muscle training menu.

[0074] The identification unit identifies the disease risks of cerebral infarction, diabetes, myocardial infarction, and aortic dissection. The identification unit identifies the disease risks of cerebral infarction, diabetes, myocardial infarction, and aortic dissection, for example, based on data related to the user's body. For example, risk factors and diagnostic criteria are used to identify these disease risks. This makes it possible to take early measures by specifically identifying the user's disease risk. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's data into an AI model to identify the disease risk.

[0075] The providing unit proposes a menu and a method for improving lifestyle habits for disease prevention. The providing unit proposes a menu and a method for improving lifestyle habits for disease prevention, for example, based on the disease risk identified by the identifying unit. The menu includes, for example, nutritional balance and selection of ingredients. The method for improving lifestyle habits includes, for example, exercise habits and sleep habits. This supports the user's health maintenance by making specific suggestions for disease prevention. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's data into an AI model to generate an optimal menu and a method for improving lifestyle habits.

[0076] The reception unit can estimate the user's emotion and adjust the timing of data input based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed to calculate the emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, more accurate data can be collected by adjusting the timing of data input 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0077] The reception unit can analyze the user's past data input history and select the optimal input method. The reception unit, for example, analyzes the user's past data input history and selects the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The reception unit can also analyze the user's past input history to determine a tendency to input data during a specific time period and prompt the user to input data during that time period. Furthermore, the reception unit can automatically complement data previously input by the user and simplify the input work. This makes it possible to provide the user with an optimal input method by analyzing the past data input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history into an AI model to generate an optimal input method.

[0078] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when inputting data. For example, when inputting the user's current health condition, the reception unit filters out abnormal values ​​by checking against past health data. The reception unit can also preferentially input highly relevant data based on the user's lifestyle habits (e.g., diet, exercise, etc.). Furthermore, if the user has set a specific health goal, the reception unit can also preferentially input data related to the goal. This makes it possible to collect more relevant data by filtering data based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current health condition and lifestyle habits into an AI model and perform filtering.

[0079] The reception unit can estimate the user's emotion and determine the priority of data to be input based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, important data can be preferentially collected 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0080] The reception unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting data. For example, when the user lives in a specific area, the reception unit preferentially inputs data related to the health risks of the area. In addition, when the user is traveling, the reception unit can also preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the reception unit can also preferentially input data related to the climate condition. In this way, data corresponding to the health risks specific to the area can be collected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model to generate highly relevant data.

[0081] The reception unit can analyze the user's social media activity and input related data at the time of data input. The reception unit automatically inputs related data based on, for example, diet and exercise information shared by the user on social media. The reception unit can also input related data from health-related accounts that the user follows on social media. Furthermore, the reception unit can also input related data from health-related groups that the user participates in on social media. In this way, data related to the user's lifestyle habits can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity into an AI model to generate related data.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. In this way, the expression method of the analysis can be adjusted according to the user's emotion, thereby providing an analysis result that is easy for the user to understand. 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 image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance and provides the data to the user. The analysis unit can also perform a simplified analysis on data with low importance and provide the data to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data and perform the analysis starting from the important data. As a result, by adjusting the level of detail of the analysis according to the importance of the data, it is possible to perform a detailed analysis on important data. 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 importance of the data into an AI model and generate the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply an algorithm that analyzes nutritional balance to dietary data. The analysis unit can also apply an algorithm that analyzes calories burned to exercise data. Furthermore, the analysis unit can apply an algorithm that analyzes disease risk to health data. This allows for more accurate analysis results to be provided by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of data into an AI model to generate an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. In this way, the length of the analysis can be adjusted according to the user's emotion, thereby providing an optimal analysis result for the user. 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 image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0086] The analysis unit can determine the priority of the analysis based on the time of data submission during the analysis. For example, the analysis unit preferentially analyzes the latest data and provides it to the user. The analysis unit can also perform a simplified analysis on old data and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the time of data submission and perform the analysis from the latest data. As a result, the analysis can be preferentially analyzed on the latest data by determining the priority of the analysis based on the time of data submission. 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 time of data submission into an AI model to generate the priority of the analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit preferentially analyzes highly relevant data and provides it to the user. The analysis unit can also perform a simplified analysis on data with low relevance and provide it to the user. Furthermore, the analysis unit can determine the order of analysis according to the relevance of data and perform analysis starting from highly relevant data. As a result, by adjusting the order of analysis based on the relevance of data, highly relevant data can be preferentially analyzed. 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 relevance of data into an AI model and generate the order of analysis.

[0088] The recommendation unit can estimate the user's emotion and adjust the way of expressing the recommendation based on the estimated emotion of the user. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The recommendation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the recommendation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, the way of expressing the recommendation can be adjusted according to the user's emotion, thereby providing a recommendation that is easy for the user to understand. 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 recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The recommendation unit can adjust the level of detail of the recommendation based on the user's health goal at the time of recommendation. For example, if the user aims to lose weight, the recommendation unit can provide a detailed recommendation regarding calorie restriction. In addition, if the user aims to increase muscle strength, the recommendation unit can also provide a detailed recommendation regarding a muscle training menu. Furthermore, if the user aims to maintain health, the recommendation unit can also provide a recommendation regarding a balanced diet and exercise. This allows the recommendation to be optimal for the user by adjusting the level of detail of the recommendation based on the user's health goal. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input the user's health goal into an AI model to generate the level of detail of the recommendation.

[0090] The recommendation unit can apply different recommendation algorithms according to the user's lifestyle at the time of recommendation. For example, if the user is busy, the recommendation unit recommends an effective exercise menu in a short time. In addition, if the user is leading a regular life, the recommendation unit can also recommend a balanced meal and exercise menu. Furthermore, if the user is leading an irregular life, the recommendation unit can also recommend an easy-to-follow meal and exercise menu. This makes it possible to provide more effective recommendations by applying an appropriate recommendation algorithm according to the user's lifestyle. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's lifestyle into an AI model and generate an appropriate recommendation algorithm.

[0091] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user's emotion. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The recommendation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the recommendation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. This makes it possible to provide the optimal recommendation for the user by adjusting the length of the recommendation 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 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 recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The recommendation unit can determine the priority order of the recommendation based on the user's past recommendation history when making a recommendation. The recommendation unit provides an optimal recommendation based on, for example, recommendations made by the user in the past. The recommendation unit can also provide a recommendation that has been highly effective from the user's past recommendation history with priority. The recommendation unit can also analyze the user's past recommendation history and provide the most effective recommendation. This allows more effective recommendations to be provided by determining the priority order of the recommendation based on the user's past recommendation history. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history into an AI model to generate a priority order of the recommendation.

[0093] The recommendation unit can adjust the order of recommendations based on the user's related data when making recommendations. The recommendation unit can provide the most effective recommendations preferentially based on the user's health data, for example. The recommendation unit can also provide optimal meal recommendations based on the user's diet data. Furthermore, the recommendation unit can also provide optimal exercise recommendations based on the user's exercise data. This allows more effective recommendations to be provided by adjusting the order of recommendations based on the user's related data. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without using AI. For example, the recommendation unit can input the user's related data into an AI model to generate the order of recommendations.

[0094] The identification unit can estimate the user's emotion and adjust the disease risk identification method based on the estimated user's emotion. For example, the identification unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The identification unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate the emotion score. Furthermore, the identification unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. In this way, the disease risk identification method can be adjusted according to the user's emotion, thereby providing a risk identification method that is easy for the user to understand. 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 identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0095] The identification unit can select an optimal identification method by analyzing the user's past health data at the time of identification. The identification unit, for example, selects the most effective risk identification method based on the user's past health data. The identification unit can also preferentially identify a specific disease risk from the user's past health data. Furthermore, the identification unit can analyze the user's past health data and select the most relevant risk identification method. In this way, an optimal disease risk identification method can be provided by analyzing the user's past health data. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's past health data into an AI model to generate an optimal identification method.

[0096] The identification unit can customize the specific means based on the user's current health condition at the time of identification. For example, when the user inputs the current health condition, the identification unit filters out abnormal values ​​by checking against past health data. The identification unit can also preferentially input highly relevant data based on the user's lifestyle (eating, exercise, etc.). Furthermore, if the user has set a specific health goal, the identification unit can also preferentially input data related to the goal. This enables more accurate disease risk identification by customizing the specific means based on the user's current health condition. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's current health condition into an AI model to generate the specific means.

[0097] The identification unit can estimate the user's emotion and determine the priority order of disease risks based on the estimated user's emotion. For example, the identification unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in facial expression. The identification unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate the emotion score. Furthermore, the identification unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, the priority order of disease risks is determined according to the user's emotion, and important risks can be preferentially identified. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0098] The identification unit can select the optimal identification method in consideration of the geographical location information of the user at the time of identification. For example, when the user lives in a specific area, the identification unit preferentially inputs data related to the health risks of the area. In addition, when the user is traveling, the identification unit can also preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the identification unit can also preferentially input data related to the climate condition. In this way, by considering the geographical location information of the user, it is possible to provide an identification method corresponding to the health risks specific to the area. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the geographical location information of the user into an AI model to generate an optimal identification method.

[0099] At the time of identification, the identification unit can analyze the social media activity of the user to identify the disease risk. The identification unit can identify the associated disease risk based on, for example, health information shared by the user on social media. The identification unit can also identify the associated disease risk from health-related accounts that the user follows on social media. Furthermore, the identification unit can also identify the associated disease risk from health-related groups that the user participates in on social media. In this way, the associated disease risk can be identified by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using AI or can be performed without using AI. For example, the identification unit can input the social media activity of the user into an AI model to identify the disease risk.

[0100] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. In this way, by adjusting the method of providing information according to the user's emotion, it is possible to provide information that is easy for the user to understand. 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0101] The providing unit can provide optimal information by referring to the user's past health data when providing information. The providing unit can provide the most effective information based on the user's past health data, for example. The providing unit can also provide information related to a specific disease risk preferentially from the user's past health data. Furthermore, the providing unit can analyze the user's past health data and provide the most relevant information. This makes it possible to provide optimal information by referring to the user's past health data. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's past health data into an AI model to generate optimal information.

[0102] The providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the change in the facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed and the emotion score is calculated. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on the fluctuation of the heart rate. As a result, the priority of information provision is determined according to the user's emotion, so that important information can be provided preferentially. 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0103] The providing unit can provide optimal information by considering the device information of the user when providing information. For example, when the user is using a smartphone, the providing unit provides an information providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide an information providing method that is concise and highly visible. This makes it possible to provide information optimized for the device by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user into an AI model to generate optimal information.

[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 data, the analysis unit can adjust the analysis results based on the user's living environment. For example, if the user lives in an urban area, the analysis can take into account the health risks specific to cities (air pollution, stress, etc.). If the user lives in a rural area, the analysis can take into account the health risks specific to rural areas (use of pesticides, lack of exercise, etc.). Furthermore, if the user is engaged in a specific occupation, the analysis can take into account the health risks specific to that occupation (long working hours, physical labor, etc.). This makes it possible to identify health risks more accurately by adjusting the analysis results based on the user's living environment.

[0106] The recommendation unit can analyze the user's meal history and make recommendations to increase meal variety. For example, if the user frequently consumes a particular ingredient, a new recipe using that ingredient can be suggested. Also, if the user is lacking in a particular nutrient, a recipe containing an ingredient to supplement that nutrient can be suggested. Furthermore, if the user has a bias toward a particular eating pattern, a balanced meal menu can be suggested to improve that pattern. In this way, by increasing the variety of meals based on the user's meal history, improvement in nutritional balance can be expected.

[0107] The identification unit can analyze the user's exercise history and provide advice for maximizing the effect of exercise. For example, if the user has been performing a particular exercise for a long period of time, a new training method for maximizing the effect of the exercise can be suggested. In addition, if the user can obtain more effective results by changing the frequency or intensity of the exercise, a specific method can be suggested. Furthermore, if the user has lost motivation for a particular exercise, a new exercise program can be suggested to restore that motivation. In this way, advice for maximizing the effect of exercise based on the user's exercise history is provided to support health maintenance.

[0108] The providing unit can provide information corresponding to seasonal health risks based on the user's health data. For example, preventive measures against influenza and colds can be suggested in winter. Also, preventive measures against heat stroke and dehydration can be suggested in summer. Furthermore, measures against allergies can be suggested in spring and autumn. In this way, by providing information corresponding to seasonal health risks, the user can take appropriate measures.

[0109] The reception unit can have a function of evaluating the reliability of input data when the user inputs data. For example, the reception unit can detect abnormal values ​​by comparing the data self-reported by the user with past data. In addition, if the data entered by the user is likely to be inaccurate, the reception unit can urge the user to recheck the data. Furthermore, if the data entered by the user lacks consistency, the reception unit can provide advice on how to correct the data. In this way, by evaluating the reliability of the input data, more accurate data analysis is possible.

[0110] The analysis unit can estimate the user's emotions and adjust the method of feedback of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis results can be provided in concise and positive terms. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is excited, relaxation methods can be suggested to calm the excitement. In this way, by adjusting the method of feedback of the analysis results according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0111] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user's emotions. For example, if the user is tired, it can recommend relaxing meals and light exercise. Also, if the user is in good health, it can recommend vigorous exercise and challenging meal menus. Furthermore, if the user is feeling stressed, it can recommend relaxation methods to reduce that stress. This allows for more effective health management by adjusting the timing of recommendations according to the user's emotions.

[0112] The identification unit can estimate the user's emotions and adjust the method of notifying the user of the disease risk based on the estimated user's emotions. For example, if the user is feeling anxious, the disease risk can be notified in a gentle manner. If the user is relaxed, detailed risk information can be provided. Furthermore, if the user is excited, risk information can be provided while suggesting a relaxation method to calm the excitement. In this way, by adjusting the method of notifying the user of the disease risk according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0113] The providing unit can estimate the user's emotion and adjust the frequency of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the frequency of information provision can be reduced and only important information can be provided. In addition, when the user is relaxed, detailed information can be provided frequently. Furthermore, when the user is excited, necessary information can be provided while providing relaxation information to calm the excitement. In this way, by adjusting the frequency of information provision according to the user's emotion, it is possible to provide optimal information for the user.

[0114] The analysis unit can estimate the user's emotions and adjust the speed of analysis based on the estimated user's emotions. For example, if the user is impatient, the analysis can be accelerated to provide the results quickly. If the user is relaxed, a detailed analysis can be performed and the results can be provided over time. Furthermore, if the user is excited, the analysis can be advanced while suggesting relaxation methods to calm the user's excitement. In this way, the analysis can be adjusted according to the user's emotions to provide the optimal analysis results for the user.

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

[0116] Step 1: The reception unit inputs data related to the user's body. The data related to the user's body includes, for example, height, weight, body fat percentage, BMI, full-body photo, blood test results, target weight / body fat percentage, diet history, exercise history, medical history, etc. The reception unit accepts the data input by the user and stores it in a database. Step 2: The analysis unit uses AI to analyze the data entered by the reception unit and compare it with the data of others. The analysis unit uses a machine learning algorithm to analyze the user's data and compare it with the data of others. Step 3: The recommendation unit recommends appropriate diet and exercise to the user based on the analysis results obtained by the analysis unit. The recommendation unit provides diet recipes and muscle training menus. Step 4: The identification unit identifies the user's disease risk based on the analysis results obtained by the analysis unit. The identification unit identifies the disease risk of cerebral infarction, diabetes, myocardial infarction, aortic dissection, etc.

[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] For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives data input by the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends appropriate diet and exercise based on the analysis result. For example, the identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's disease risk. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the user with information for disease prevention. 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] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives data input by the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends appropriate diet and exercise based on the analysis result. For example, the identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's disease risk. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the user with information for disease prevention. 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 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.

[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] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives data input by the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends appropriate diet and exercise based on the analysis results. For example, the identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's disease risk. For example, the provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the user with information for disease prevention. 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] For example, the reception unit is realized by the microphone 238 of the robot 414 and receives data input by the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends appropriate diet and exercise based on the analysis results. For example, the identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's disease risk. For example, the provision unit is realized by the speaker 240 of the robot 414 and provides the user with information for disease prevention. 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 reception unit for inputting data related to a user's body; an analysis unit that analyzes the data input by the reception unit and compares it with data of other people; a recommendation unit that recommends a diet and exercise based on specific criteria to the user based on the analysis result obtained by the analysis unit; and an identification unit that identifies a disease risk of the user based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) a providing unit that provides information indicating the disease risk identified by the identifying unit to the user; 2. The system of claim 1, (Appendix 3) The reception unit is Enter your height, weight, body fat percentage, BMI, full body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history. 2. The system of claim 1, (Appendix 4) The recommendation unit is Providing diet recipes or muscle training menus 2. The system of claim 1, (Appendix 5) The identification unit is Identify disease risks for stroke, diabetes, myocardial infarction, and aortic dissection 2. The system of claim 1, (Appendix 6) The providing unit is Suggest ways to improve your diet and lifestyle to prevent illness 3. The system of claim 2, (Appendix 7) The reception unit is Infer user emotions and adjust the timing of data input based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past data entry history and select the optimal entry method 2. The system of claim 1, (Appendix 9) The reception unit is Filter data entry based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input data based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is During data entry, the system takes into account the user's geographic location to prioritize the most relevant data. 2. The system of claim 1, (Appendix 12) The reception unit is During data entry, analyze users' social media activity and enter relevant data 2. The system of claim 1, (Appendix 13) 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 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) 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 17) The analysis unit is During analysis, prioritize analysis based on when data were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The recommendation unit is Infer user emotions and adjust the way recommendations are presented based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The recommendation unit is When making recommendations, adjust the level of detail of the recommendations based on the user's health goals. 2. The system of claim 1, (Appendix 21) The recommendation unit is When making recommendations, different recommendation algorithms are applied depending on the user's lifestyle. 2. The system of claim 1, (Appendix 22) The recommendation unit is Infer user sentiment and adjust the length of recommendations based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The recommendation unit is When making recommendations, the priority of recommendations is determined based on the user's past recommendation history. 2. The system of claim 1, (Appendix 24) The recommendation unit is When making recommendations, adjust the order of recommendations based on the user's relevant data 2. The system of claim 1, (Appendix 25) The identification unit is Inferring a user's emotions and adjusting a method for identifying disease risk based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The identification unit is At the time of identification, the system analyzes the user's past health data to select the optimal identification method. 2. The system of claim 1, (Appendix 27) The identification unit is Upon identification, customize specific measures based on the user's current health status 2. The system of claim 1, (Appendix 28) The identification unit is Estimating user emotions and prioritizing disease risks based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The identification unit is When identifying a user, the optimal identification method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The identification unit is Upon identification, the user's social media activity will be analyzed to identify disease risk. 2. The system of claim 1, (Appendix 31) The providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 3. The system of claim 2, (Appendix 32) The providing unit is When providing information, the system provides optimal information by referencing the user's past health data. 3. The system of claim 2, (Appendix 33) The providing unit is Estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. 3. The system of claim 2, (Appendix 34) The providing unit is When providing information, provide optimal information by taking into account the user's device information 3. The system of claim 2, [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 reception unit for inputting data related to a user's body; an analysis unit that analyzes the data input by the reception unit and compares it with data of other people; a recommendation unit that recommends a diet and exercise based on specific criteria to the user based on the analysis result obtained by the analysis unit; and an identification unit that identifies a disease risk of the user based on the analysis result obtained by the analysis unit. A system characterized by:

2. a providing unit that provides information indicating the disease risk identified by the identifying unit to the user; 2. The system of claim 1 .

3. The reception unit is Enter your height, weight, body fat percentage, BMI, full body photo, blood test results, target weight / body fat percentage, diet history, exercise history, and medical history.

2. The system of claim 1 .

4. The recommendation unit is Providing diet recipes or muscle training menus 2. The system of claim 1 .

5. The identification unit is Identify disease risks for stroke, diabetes, myocardial infarction, and aortic dissection 2. The system of claim 1 .

6. The providing unit is Suggest ways to improve your diet and lifestyle to prevent illness 3. The system of claim 2.

7. The reception unit is The user's emotion is estimated, and a timing of data input is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyzing the user's past data entry history and selecting the optimal entry method 2. The system of claim 1 .

9. The reception unit is When data is entered, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1 .

10. The reception unit is Estimating the user's emotion and determining a priority order of data to be input based on the estimated user's emotion.

2. The system of claim 1 .

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