System

Abstract

To provide a system according to an embodiment that provides a wellness prediction, or advice on the basis of behavior data on a user.SOLUTION: A system according to an embodiment comprises: a data collection unit; an analysis unit; a prediction unit; and an advice unit. The data collection unit is configured to collect behavior data on a user. The analysis unit is configured to analyze data collected by the data collection unit, and learn a behavior pattern. The prediction unit is configured to predict an easy-to-get sickness or an onset time of the sickness, and a cure rate on the basis of the behavior pattern learned by the analysis unit. The advice unit is configured to advise a daily wellness advice on the basis of a result predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technologies do not adequately provide health predictions or advice based on users' behavioral data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide health predictions and advice based on user behavior data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and an advice unit. The data collection unit collects behavioral data of a user. The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The prediction unit predicts likely diseases, their onset time, and cure rate based on the behavioral patterns learned by the analysis unit. The advice unit provides daily health advice based on the results predicted by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can provide health predictions and advice based on user behavior data. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

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

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system that contributes to maintaining health by learning a user's behavioral patterns and predicting diseases that the user is likely to develop, their onset time, and cure rate based on the behavioral tendencies of each individual user. This system provides personalized daily health advice based on not only long-term trends but also recent lifestyle habits. For example, the health management system collects user behavioral data. For example, data such as meal contents, exercise frequency, and sleep time are collected. This data may be manually entered by the user or may be automatically collected from devices such as wearable devices and mobile devices. Next, the collected data is analyzed by AI. The AI ​​learns the user's behavioral patterns and predicts diseases that the user is likely to develop, their onset time, and cure rate. For example, the risk of diabetes and heart disease can be predicted from the meal contents and exercise frequency. Furthermore, the AI ​​provides daily health advice to the user based on the prediction results. For example, it provides advice on improving the diet, recommendations for exercise, and advice on improving the quality of sleep. This advice is provided in an interactive format, so the user can manage their health while interacting with the AI. This allows the user to understand their own health condition and take appropriate measures. For example, if AI predicts the risk of diabetes, the user can reduce the risk by following the recommendations for improving diet and exercise. In addition, by following the daily health advice provided by AI, the user can easily maintain his / her health. The health management system not only provides long-term health management but also daily health advice based on the user's recent lifestyle habits, so the user can always obtain the latest health information. For example, the AI ​​can provide appropriate advice based on the user's recent diet and exercise frequency, making it easier for the user to maintain his / her health. In this way, the health management system can learn the user's behavioral patterns and predict the diseases that the individual user is likely to develop based on their behavioral tendencies, the time of onset, and the cure rate, thereby contributing to the maintenance of health.

[0029] The health management system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and an advice unit. The data collection unit collects behavioral data of the user. The behavioral data of the user includes, but is not limited to, for example, meal contents, exercise frequency, and sleep time. The data collection unit collects, for example, data manually input by the user. The data collection unit can also automatically collect data from devices such as wearable devices and mobile devices. For example, the data collection unit collects heart rate and step count data from a wearable device. The data collection unit can also collect meal photos and exercise records from a mobile device. The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit analyzes data using, for example, AI. The AI ​​learns behavioral patterns based on the behavioral data of the user. For example, the AI ​​learns patterns for predicting health risks from the user's meal contents and exercise frequency. The prediction unit predicts diseases that the user is likely to develop, their onset time, and cure rate based on the behavioral patterns learned by the analysis unit. The prediction unit performs prediction using, for example, AI. The AI ​​predicts diseases that the user is likely to develop, the time of onset, and the cure rate based on the user's behavioral patterns. For example, the AI ​​can predict the risk of diabetes or heart disease based on the user's diet and exercise frequency. The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit provides advice using, for example, AI. The AI ​​provides daily health advice based on the user's behavioral data and prediction results. For example, the AI ​​provides advice on how to improve the user's diet, recommendations for exercise, and advice on improving the quality of sleep. As a result, the health management system according to the embodiment can contribute to maintaining health by collecting, analyzing, and predicting the user's behavioral data and providing health advice.

[0030] The data collection unit collects behavioral data of the user. The user's behavioral data includes, but is not limited to, for example, meal contents, exercise frequency, and sleep time. The data collection unit collects, for example, data manually input by the user. The user can manually input daily meal contents, exercise records, sleep time, and the like using a dedicated application. This application has an easy-to-use interface and is designed to allow the user to easily input data. The data collection unit can also automatically collect data from devices such as wearable devices and mobile devices. For example, the data collection unit collects heart rate and step count data from a wearable device. The wearable device is worn on the user's wrist or chest and monitors data such as heart rate, step count, and calories burned 24 hours a day. These data are transmitted to the data collection unit via Bluetooth (registered trademark) or Wi-Fi. The data collection unit can also collect photos of meals and exercise records from mobile devices. The user can take photos of meals using the camera of their smartphone and upload them to the application. The application uses image recognition technology to analyze the contents of meals and automatically extract information on calories and nutrients. In addition, it can use the GPS function of the mobile device to collect data such as the user's exercise route, distance, and time. This allows the data collection unit to collect user behavioral data from multiple angles and provide detailed health information.

[0031] The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit analyzes the data using, for example, AI. The AI ​​learns behavioral patterns based on the user's behavioral data. Specifically, the AI ​​uses a machine learning algorithm to analyze data such as the user's diet, exercise frequency, and sleep time, and learns patterns for predicting health risks. For example, the AI ​​evaluates the nutritional balance and calorie intake from the user's diet and identifies the risk of excessive calorie intake and nutritional deficiency. It also analyzes the frequency and intensity of exercise to evaluate the risk of insufficient exercise and excessive exercise. It also analyzes the sleep time and quality of sleep to identify the risk of insufficient sleep and irregular sleep patterns. This allows the analysis unit to learn detailed behavioral patterns for predicting health risks based on the user's behavioral data. Furthermore, the analysis unit can also use past data and statistical information to evaluate and analyze trends in long-term health risks. For example, it predicts fluctuations in health risks in specific seasons and time periods based on past data, and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The prediction unit predicts diseases that the user is likely to develop, their onset time, cure rate, etc., based on the behavioral patterns learned by the analysis unit. The prediction unit makes predictions using, for example, AI. The AI ​​predicts diseases that the user is likely to develop, their onset time, cure rate, etc., based on the user's behavioral patterns. Specifically, the AI ​​analyzes data such as the user's diet, exercise frequency, and sleep time, and predicts the risk of diabetes, heart disease, obesity, high blood pressure, etc. For example, the AI ​​evaluates the intake of sugar and fat from the content of the user's diet and predicts the risk of diabetes and heart disease. In addition, the AI ​​analyzes the frequency and intensity of exercise to evaluate health risks due to lack of exercise and excessive exercise. Furthermore, the AI ​​analyzes the sleep time and quality of sleep to identify health risks due to lack of sleep and irregular sleep patterns. As a result, the prediction unit can predict diseases that the user is likely to develop, their onset time, cure rate, etc., with high accuracy based on the user's behavioral data. Furthermore, the prediction unit can continuously correct the prediction results based on data updated in real time to respond to the latest situation. For example, if the user's behavioral data changes, the prediction unit immediately takes in new data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessments by taking into account the characteristics of each region and past health history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, and to support rapid and appropriate responses.

[0033] The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit provides advice using, for example, AI. The AI ​​provides daily health advice based on the user's behavioral data and the prediction results. Specifically, the AI ​​provides the user with advice on how to improve their diet, recommendations for exercise, and advice for improving sleep quality. For example, the AI ​​analyzes the user's diet and suggests specific ingredients and recipes for improving nutritional balance. It also analyzes the frequency and intensity of exercise and provides an exercise plan according to the user's physical strength and health condition. Furthermore, as advice for improving sleep quality, it suggests ways to relax before going to bed and how to prepare an appropriate sleeping environment. This allows the advice unit to provide specific advice that is useful for maintaining and improving the user's health. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. For example, by the user reporting the results of following the advice, the AI ​​evaluates the effectiveness and reflects it in the next advice. In addition, the advice unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the advice section to provide the user with health advice quickly and reliably, thereby contributing to maintaining good health.

[0034] The data collection unit can collect data on the user's meal contents, exercise frequency, and sleep time. The data collection unit, for example, collects the user's meal contents. The meal contents include, for example, calories, nutrients, and types of meals, but are not limited to such examples. The data collection unit collects, for example, meal contents manually input by the user. The data collection unit can also automatically collect meal contents from a wearable device or a mobile device. For example, the data collection unit collects photos of meals from a mobile device and analyzes the meal contents using AI. The frequency of exercise includes, for example, how many times a week the user exercised, the type of exercise, and the like, but are not limited to such examples. The data collection unit collects, for example, the frequency of exercise manually input by the user. The data collection unit can also automatically collect the frequency of exercise from a wearable device. For example, the data collection unit collects records of exercise from a wearable device and analyzes the frequency of exercise using AI. The sleep time includes, for example, the nighttime sleep time, the time of a nap, and the like, but are not limited to such examples. The data collection unit collects, for example, the sleep time manually input by the user. The data collection unit can also automatically collect sleep time from the wearable device. For example, the data collection unit collects sleep records from the wearable device and analyzes the sleep time using AI. This allows the data collection unit to collect the user's diet, exercise, and sleep data, enabling more detailed health management.

[0035] The advice unit can provide health advice in an interactive manner. The advice unit can provide health advice in an interactive manner, for example, by using a chatbot. The chatbot provides health advice through a dialogue with the user. For example, the chatbot can provide advice on dietary improvements, exercise recommendations, and advice on improving sleep quality in response to a user's question. The advice unit can also provide health advice in an interactive manner by using a voice assistant. The voice assistant can provide health advice in response to a user's voice input. For example, the voice assistant can provide advice on dietary improvements, exercise recommendations, and advice on improving sleep quality based on the user's voice input. As a result, the advice unit can provide health advice in an interactive manner, allowing the user to manage their health more effectively.

[0036] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral data. The past behavioral data includes, for example, data from the past year, specific event data, etc., but is not limited to such examples. The data collection unit, for example, analyzes the past behavioral data using AI. The AI ​​selects the optimal data collection method based on the user's past behavioral data. For example, the data collection unit proposes a method of automatically collecting data from a wearable device based on data manually input by the user in the past. The data collection unit can also preferentially collect data from a device that the user has frequently used in the past. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. Thereby, the data collection unit can select the optimal data collection method by analyzing the past behavioral data. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the past behavioral data to the generation AI and cause the generation AI to select the optimal data collection method.

[0037] The data collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. The data collection unit, for example, filters data taking into consideration the user's current living situation. The living situation includes, for example, whether or not the user has a job, the home environment, and the like, but is not limited to such examples. For example, if the user is working, the data collection unit preferentially collects data related to the work. In addition, if the user is interested in the home environment, the data collection unit can also preferentially collect data related to the home environment. Furthermore, the data collection unit filters data based on the user's field of interest. The field of interest includes, for example, hobbies, topics of interest, and the like, but is not limited to such examples. For example, if the user is on a diet, the data collection unit preferentially collects data related to the contents of meals. In addition, if the user is interested in exercise, the data collection unit can also preferentially collect data related to the frequency and type of exercise. In addition, if the user wants to improve the quality of sleep, the data collection unit can also preferentially collect data related to sleep time and quality. In this way, the data collection unit can collect more relevant data by filtering data based on the user's living situation and field of interest. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0038] The data collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. The data collection unit collects data, for example, taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is traveling, the data collection unit preferentially collects data related to meals and exercise at the travel destination. In addition, when the user is at home, the data collection unit can also preferentially collect data related to daily life. Furthermore, when the user is at work, the data collection unit can also preferentially collect data related to the work environment. Thereby, the data collection unit can collect highly relevant data preferentially by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The data collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The data collection unit, for example, analyzes the social media activity of the user. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The data collection unit, for example, uses AI to analyze the social media activity. The AI ​​collects related data based on the social media activity of the user. For example, the data collection unit collects data on meal contents based on photos of meals shared by the user on social media. The data collection unit can also collect data on the frequency and type of exercise based on the records of exercise shared by the user on social media. Furthermore, the data collection unit can collect data on sleep time and quality based on the records of sleep shared by the user on social media. In this way, the data collection unit can collect related data by analyzing the social media activity. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the social media activity of the user to the generation AI and cause the generation AI to collect related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data, for example. The importance of the behavioral data includes, for example, the level of health risk, frequency, etc., but is not limited to such examples. The analysis unit evaluates the importance of the behavioral data, for example, using AI. The AI ​​adjusts the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data. In addition, the analysis unit can perform a simplified analysis on behavioral data with low importance. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the behavioral data. As a result, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. The analysis unit applies different analysis algorithms depending on the category of behavioral data, for example. The categories of behavioral data include, but are not limited to, exercise data, dietary data, and sleep data. The analysis unit classifies the category of behavioral data using, for example, AI. The AI ​​applies an appropriate analysis algorithm depending on the category of behavioral data. For example, the analysis unit applies a nutrient analysis algorithm to dietary data. In addition, the analysis unit can apply an exercise intensity analysis algorithm to exercise data. Furthermore, the analysis unit can also apply a sleep quality analysis algorithm to sleep data. This allows the analysis unit to apply different analysis algorithms depending on the category of behavioral data, thereby enabling more accurate analysis. 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 category of behavioral data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of the analysis based on the collection time of the behavioral data during the analysis. The analysis unit determines the priority of the analysis based on, for example, the collection time of the behavioral data. The collection time of the behavioral data includes, for example, recent data, past data, and the like, but is not limited to such examples. The analysis unit evaluates the collection time of the behavioral data using, for example, AI. The AI ​​determines the priority of the analysis based on the collection time of the behavioral data. For example, the analysis unit preferentially analyzes the recently collected data. In addition, the analysis unit can analyze an overall trend based on long-term data. Furthermore, the analysis unit can also analyze a trend for each period based on data collected in a specific period. Thereby, the analysis unit can preferentially analyze the latest data by determining the priority of the analysis based on the collection time of the behavioral data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the behavioral data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the behavioral data. The relevance of the behavioral data includes, for example, correlation and causal relationship, but is not limited to such examples. The analysis unit, for example, uses AI to evaluate the relevance of the behavioral data. The AI ​​adjusts the order of analysis based on the relevance of the behavioral data. For example, the analysis unit preferentially analyzes data with high relevance. In addition, the analysis unit can analyze data with low relevance later. Furthermore, the analysis unit can also determine the order of analysis according to the relevance of the data. As a result, the analysis unit adjusts the order of analysis based on the relevance of the behavioral data, thereby enabling efficient analysis. 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 the behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] The prediction unit can improve the accuracy of prediction by considering the interrelationship of behavioral data at the time of prediction. The prediction unit, for example, makes a prediction by considering the interrelationship of behavioral data. The interrelationship of behavioral data includes, for example, the relationship between exercise and food, the relationship between sleep and stress, and the like, but is not limited to such examples. The prediction unit, for example, uses AI to evaluate the interrelationship of behavioral data. The AI ​​improves the accuracy of prediction based on the interrelationship of behavioral data. For example, the prediction unit predicts the risk of disease by considering the interrelationship between food data and exercise data. In addition, the prediction unit can predict the health risk by considering the interrelationship between sleep data and stress data. Furthermore, the prediction unit can predict the overall health risk by considering the interrelationship of all behavioral data. Thereby, the prediction unit can improve the accuracy of prediction by considering the interrelationship of behavioral data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the interrelationship of behavioral data to the generation AI and cause the generation AI to execute the improvement of the accuracy of prediction.

[0045] The prediction unit can make a prediction by taking into consideration the attribute information of the submitter of the behavioral data at the time of prediction. The prediction unit makes a prediction by taking into consideration the attribute information of the submitter of the behavioral data, for example. The attribute information of the submitter includes, for example, age, gender, occupation, etc., but is not limited to such examples. The prediction unit evaluates the attribute information of the submitter using, for example, AI. The AI ​​makes a prediction based on the attribute information of the submitter. For example, the prediction unit predicts a disease risk according to the age by taking into consideration the age of the user. In addition, the prediction unit can predict a disease risk according to the gender by taking into consideration the gender of the user. Furthermore, the prediction unit can predict a health risk according to the occupation by taking into consideration the occupation of the user. This enables the prediction unit to make a more individualized prediction by taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the attribute information of the submitter to the generation AI and cause the generation AI to execute the prediction.

[0046] The prediction unit can make a prediction taking into account the geographical distribution of the behavioral data at the time of prediction. The prediction unit makes a prediction taking into account, for example, the geographical distribution of the behavioral data. The geographical distribution includes, for example, health risks for each region, geographical clusters, and the like, but is not limited to such examples. The prediction unit, for example, evaluates the geographical distribution using AI. The AI ​​makes a prediction based on the geographical distribution. For example, the prediction unit makes a prediction taking into account the incidence rate of a disease in the region where the user lives. The prediction unit can also predict the health risk of a travel destination where the user travels. Furthermore, the prediction unit can also predict the health risk for each region based on the user's range of movement. As a result, the prediction unit can predict the health risk for each region by taking the geographical distribution into account. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data of the geographical distribution to the generation AI and cause the generation AI to execute the prediction.

[0047] The prediction unit can improve the accuracy of the prediction by referring to related literature of the behavioral data at the time of prediction. The prediction unit, for example, makes a prediction by referring to related literature of the behavioral data. The related literature includes, for example, academic papers, technical reports, etc., but is not limited to such examples. The prediction unit, for example, evaluates the related literature using AI. The AI ​​improves the accuracy of the prediction based on the related literature. For example, the prediction unit predicts the risk of disease by referring to the latest medical research. In addition, the prediction unit can predict health risks based on past research data. Furthermore, the prediction unit can also improve the reliability of the prediction result by referring to related literature. Thereby, the prediction unit can improve the accuracy of the prediction by referring to the related literature. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input data of related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the prediction.

[0048] The advice unit can adjust the level of detail of the advice based on the importance of the prediction result when providing the advice. The advice unit adjusts the level of detail of the advice based on the importance of the prediction result, for example. The importance of the prediction result includes, but is not limited to, the level of risk and the degree of impact. The advice unit, for example, evaluates the importance of the prediction result using an AI. The AI ​​adjusts the level of detail of the advice based on the importance of the prediction result. For example, the advice unit provides detailed advice for an important prediction result. In addition, for a prediction result with low importance, simplified advice can be provided. Furthermore, the advice unit can also determine the priority of advice according to the importance of the prediction result. As a result, the advice unit adjusts the level of detail of the advice based on the importance of the prediction result, thereby enabling efficient advice. Part or all of the above-mentioned processing in the advice unit may be performed using an AI, for example, or may be performed without using an AI. For example, the advice unit can input the importance of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0049] The advice unit can apply different advice algorithms according to the category of the prediction result when providing advice. The advice unit applies different advice algorithms according to the category of the prediction result, for example. The category of the prediction result includes, but is not limited to, for example, the type of disease and the type of risk. The advice unit classifies the category of the prediction result using, for example, AI. The AI ​​applies an appropriate advice algorithm according to the category of the prediction result. For example, the advice unit applies a nutrition advice algorithm to the prediction result regarding diet. In addition, the advice unit can apply an exercise advice algorithm to the prediction result regarding exercise. Furthermore, the advice unit can also apply a sleep advice algorithm to the prediction result regarding sleep. As a result, the advice unit can apply different advice algorithms according to the category of the prediction result, thereby making it possible to provide more accurate advice. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the category of the prediction result to the generation AI and cause the generation AI to apply an appropriate advice algorithm.

[0050] The advice unit can determine the priority of advice based on the submission time of the prediction result when providing advice. The advice unit determines the priority of advice based on, for example, the submission time of the prediction result. The submission time of the prediction result includes, for example, a recent prediction result, a past prediction result, and the like, but is not limited to such examples. The advice unit evaluates the submission time of the prediction result using, for example, an AI. The AI ​​determines the priority of advice based on the submission time of the prediction result. For example, the advice unit provides advice preferentially based on the recent prediction result. In addition, the advice unit can provide overall advice based on a long-term prediction result. Furthermore, the advice unit can provide advice for each period based on the prediction result submitted in a specific period. Thereby, the advice unit can provide the latest information preferentially by determining the priority of advice based on the submission time of the prediction result. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the submission time of the prediction result to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0051] The advice unit can adjust the order of advice based on the relevance of the prediction result when providing advice. The advice unit adjusts the order of advice based on, for example, the relevance of the prediction result. The relevance of the prediction result includes, for example, correlation, causal relationship, and the like, but is not limited to such examples. The advice unit evaluates the relevance of the prediction result using, for example, an AI. The AI ​​adjusts the order of advice based on the relevance of the prediction result. For example, the advice unit provides advice preferentially based on a highly relevant prediction result. In addition, the advice unit can provide advice later based on a less relevant prediction result. Furthermore, the advice unit can also determine the order of advice according to the relevance of the prediction result. As a result, the advice unit adjusts the order of advice based on the relevance of the prediction result, thereby enabling efficient advice. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the relevance of the prediction result to the generation AI and cause the generation AI to adjust the order of advice.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The health management system can also collect social network data of the user and analyze the data in the analysis unit. For example, the frequency and content of interactions with the user's friends and family can be analyzed to evaluate the presence or absence of social support. This can provide advice to increase social activity if the user is isolated. Also, if the user is feeling stressed, it can recommend interactions with trusted friends and family. Furthermore, health risks can be predicted based on the user's social network data. For example, if the health of friends and family is deteriorating, the user's own health risk may also increase, so advice can be given to take early measures.

[0054] The data collection unit can also collect purchase history data of the user and analyze the data with the analysis unit. For example, data on foods and supplements purchased by the user can be collected to evaluate the nutritional balance. This can be used to advise the user to reduce their intake if they are consuming too much of a particular nutrient. Also, if the user has purchased healthy foods, it can be recommended that they continue that behavior. Furthermore, health risks can be predicted based on the user's purchase history data. For example, if the user frequently purchases high-calorie foods, it can be recommended that they improve their diet because they are at higher risk of obesity and diabetes.

[0055] The data collection unit can analyze the user's past behavioral data and select the optimal timing for data collection. For example, the time period in which the user input data in the past is analyzed, and data collection is performed during that time period. In addition, the data collection unit can analyze the days of the week and dates on which the user found it easy to input data in the past, and collect data on those days of the week and dates. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient timing for data collection. In this way, the data collection unit can select the optimal timing for data collection by analyzing past behavioral data.

[0056] The data collection unit can adjust the method of data collection based on the user's current living situation and areas of interest. For example, if the user is working, data related to work can be collected preferentially. Also, if the user is interested in the home environment, data related to the home environment can be collected preferentially. Furthermore, if the user is on a diet, data on the contents of meals can be collected preferentially. In this way, the data collection unit can collect more relevant data by adjusting the method of data collection based on the user's living situation and areas of interest.

[0057] The data collection unit can adjust the method of data collection based on the geographical location information of the user. For example, when the user is traveling, data related to meals and exercise at the travel destination can be collected with priority. When the user is at home, data related to daily life can be collected with priority. Furthermore, when the user is at work, data related to the work environment can be collected with priority. In this way, the data collection unit can collect highly relevant data by adjusting the method of data collection based on the geographical location information of the user.

[0058] The data collection unit can analyze the user's social media activity and collect related data. For example, data on meal contents can be collected based on photos of meals shared by the user on social media. Data on the frequency and type of exercise can also be collected based on exercise records shared by the user on social media. Furthermore, data on sleep duration and quality can also be collected based on sleep records shared by the user on social media. In this way, the data collection unit can collect related data by analyzing social media activity.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The data collection unit collects user behavior data. The user behavior data includes food content, exercise frequency, sleep time, etc. The data collection unit collects data manually entered by the user and data automatically collected from wearable devices and mobile devices. For example, it collects heart rate and step count data from wearable devices and collects food photos and exercise records from mobile devices. Step 2: The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit uses AI to analyze the data and learns behavioral patterns based on the user's behavioral data. For example, it learns patterns for predicting health risks from the user's diet and exercise frequency. Step 3: The prediction unit predicts what diseases the user is likely to develop, when they will develop, and the cure rate based on the behavioral patterns learned by the analysis unit. The prediction unit uses AI to make predictions and predicts the risk of diabetes and heart disease based on the user's behavioral patterns. Step 4: The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit uses AI to provide advice on improving diet, exercise, and sleep quality based on the user's behavioral data and prediction results.

[0061] (Example 2) A health management system according to an embodiment of the present invention is a system that contributes to maintaining health by learning a user's behavioral patterns and predicting diseases that the user is likely to develop, their onset time, and cure rate based on the behavioral tendencies of each individual user. This system provides personalized daily health advice based on not only long-term trends but also recent lifestyle habits. For example, the health management system collects user behavioral data. For example, data such as meal contents, exercise frequency, and sleep time are collected. This data may be manually entered by the user or may be automatically collected from devices such as wearable devices and mobile devices. Next, the collected data is analyzed by AI. The AI ​​learns the user's behavioral patterns and predicts diseases that the user is likely to develop, their onset time, and cure rate. For example, the risk of diabetes and heart disease can be predicted from the meal contents and exercise frequency. Furthermore, the AI ​​provides daily health advice to the user based on the prediction results. For example, it provides advice on improving the diet, recommendations for exercise, and advice on improving the quality of sleep. This advice is provided in an interactive format, so the user can manage their health while interacting with the AI. This allows the user to understand their own health condition and take appropriate measures. For example, if AI predicts the risk of diabetes, the user can reduce the risk by following the recommendations for improving diet and exercise. In addition, by following the daily health advice provided by AI, the user can easily maintain his / her health. The health management system not only provides long-term health management but also daily health advice based on the user's recent lifestyle habits, so the user can always obtain the latest health information. For example, the AI ​​can provide appropriate advice based on the user's recent diet and exercise frequency, making it easier for the user to maintain his / her health. In this way, the health management system can learn the user's behavioral patterns and predict the diseases that the individual user is likely to develop based on their behavioral tendencies, the time of onset, and the cure rate, thereby contributing to the maintenance of health.

[0062] The health management system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and an advice unit. The data collection unit collects behavioral data of the user. The behavioral data of the user includes, but is not limited to, for example, meal contents, exercise frequency, and sleep time. The data collection unit collects, for example, data manually input by the user. The data collection unit can also automatically collect data from devices such as wearable devices and mobile devices. For example, the data collection unit collects heart rate and step count data from a wearable device. The data collection unit can also collect meal photos and exercise records from a mobile device. The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit analyzes data using, for example, AI. The AI ​​learns behavioral patterns based on the behavioral data of the user. For example, the AI ​​learns patterns for predicting health risks from the user's meal contents and exercise frequency. The prediction unit predicts diseases that the user is likely to develop, their onset time, and cure rate based on the behavioral patterns learned by the analysis unit. The prediction unit performs prediction using, for example, AI. The AI ​​predicts diseases that the user is likely to develop, the time of onset, and the cure rate based on the user's behavioral patterns. For example, the AI ​​can predict the risk of diabetes or heart disease based on the user's diet and exercise frequency. The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit provides advice using, for example, AI. The AI ​​provides daily health advice based on the user's behavioral data and prediction results. For example, the AI ​​provides advice on how to improve the user's diet, recommendations for exercise, and advice on improving the quality of sleep. As a result, the health management system according to the embodiment can contribute to maintaining health by collecting, analyzing, and predicting the user's behavioral data and providing health advice.

[0063] The data collection unit collects user behavior data. The user behavior data includes, but is not limited to, for example, meal contents, exercise frequency, and sleep time. The data collection unit collects, for example, data manually input by the user. The user can manually input daily meal contents, exercise records, sleep time, and the like using a dedicated application. This application has an easy-to-use interface and is designed to allow the user to easily input data. The data collection unit can also automatically collect data from devices such as wearable devices and mobile devices. For example, the data collection unit collects heart rate and step count data from a wearable device. The wearable device is worn on the user's wrist or chest and monitors data such as heart rate, step count, and calories burned 24 hours a day. These data are transmitted to the data collection unit via Bluetooth or Wi-Fi. The data collection unit can also collect meal photos and exercise records from a mobile device. The user can take photos of meals using the camera of their smartphone and upload them to the application. The application uses image recognition technology to analyze the meal contents and automatically extract calorie and nutrient information. Furthermore, by utilizing the GPS function of the mobile device, data such as the user's exercise route, distance, time, etc. can be collected. This allows the data collection unit to collect user behavior data from multiple angles and provide detailed health information.

[0064] The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit analyzes the data using, for example, AI. The AI ​​learns behavioral patterns based on the user's behavioral data. Specifically, the AI ​​uses a machine learning algorithm to analyze data such as the user's diet, exercise frequency, and sleep time, and learns patterns for predicting health risks. For example, the AI ​​evaluates the nutritional balance and calorie intake from the user's diet and identifies the risk of excessive calorie intake and nutritional deficiency. It also analyzes the frequency and intensity of exercise to evaluate the risk of insufficient exercise and excessive exercise. It also analyzes the sleep time and quality of sleep to identify the risk of insufficient sleep and irregular sleep patterns. This allows the analysis unit to learn detailed behavioral patterns for predicting health risks based on the user's behavioral data. Furthermore, the analysis unit can also use past data and statistical information to evaluate and analyze trends in long-term health risks. For example, it predicts fluctuations in health risks in specific seasons and time periods based on past data, and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0065] The prediction unit predicts diseases that the user is likely to develop, their onset time, cure rate, etc., based on the behavioral patterns learned by the analysis unit. The prediction unit makes predictions using, for example, AI. The AI ​​predicts diseases that the user is likely to develop, their onset time, cure rate, etc., based on the user's behavioral patterns. Specifically, the AI ​​analyzes data such as the user's diet, exercise frequency, and sleep time, and predicts the risk of diabetes, heart disease, obesity, high blood pressure, etc. For example, the AI ​​evaluates the intake of sugar and fat from the content of the user's diet and predicts the risk of diabetes and heart disease. In addition, the AI ​​analyzes the frequency and intensity of exercise to evaluate health risks due to lack of exercise and excessive exercise. Furthermore, the AI ​​analyzes the sleep time and quality of sleep to identify health risks due to lack of sleep and irregular sleep patterns. As a result, the prediction unit can predict diseases that the user is likely to develop, their onset time, cure rate, etc., with high accuracy based on the user's behavioral data. Furthermore, the prediction unit can continuously correct the prediction results based on data updated in real time to respond to the latest situation. For example, if the user's behavioral data changes, the prediction unit immediately takes in new data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessments by taking into account the characteristics of each region and past health history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, and to support rapid and appropriate responses.

[0066] The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit provides advice using, for example, AI. The AI ​​provides daily health advice based on the user's behavioral data and the prediction results. Specifically, the AI ​​provides the user with advice on how to improve their diet, recommendations for exercise, and advice for improving sleep quality. For example, the AI ​​analyzes the user's diet and suggests specific ingredients and recipes for improving nutritional balance. It also analyzes the frequency and intensity of exercise and provides an exercise plan according to the user's physical strength and health condition. Furthermore, as advice for improving sleep quality, it suggests ways to relax before going to bed and how to prepare an appropriate sleeping environment. This allows the advice unit to provide specific advice that is useful for maintaining and improving the user's health. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. For example, by the user reporting the results of following the advice, the AI ​​evaluates the effectiveness and reflects it in the next advice. In addition, the advice unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the advice section to provide the user with health advice quickly and reliably, thereby contributing to maintaining good health.

[0067] The data collection unit can collect data on the user's meal contents, exercise frequency, and sleep time. The data collection unit, for example, collects the user's meal contents. The meal contents include, for example, calories, nutrients, and types of meals, but are not limited to such examples. The data collection unit collects, for example, meal contents manually input by the user. The data collection unit can also automatically collect meal contents from a wearable device or a mobile device. For example, the data collection unit collects photos of meals from a mobile device and analyzes the meal contents using AI. The frequency of exercise includes, for example, how many times a week the user exercised, the type of exercise, and the like, but are not limited to such examples. The data collection unit collects, for example, the frequency of exercise manually input by the user. The data collection unit can also automatically collect the frequency of exercise from a wearable device. For example, the data collection unit collects records of exercise from a wearable device and analyzes the frequency of exercise using AI. The sleep time includes, for example, the nighttime sleep time, the time of a nap, and the like, but are not limited to such examples. The data collection unit collects, for example, the sleep time manually input by the user. The data collection unit can also automatically collect sleep time from the wearable device. For example, the data collection unit collects sleep records from the wearable device and analyzes the sleep time using AI. This allows the data collection unit to collect the user's diet, exercise, and sleep data, enabling more detailed health management.

[0068] The advice unit can provide health advice in an interactive manner. The advice unit can provide health advice in an interactive manner, for example, by using a chatbot. The chatbot provides health advice through a dialogue with the user. For example, the chatbot can provide advice on dietary improvements, exercise recommendations, and advice on improving sleep quality in response to a user's question. The advice unit can also provide health advice in an interactive manner by using a voice assistant. The voice assistant can provide health advice in response to a user's voice input. For example, the voice assistant can provide advice on dietary improvements, exercise recommendations, and advice on improving sleep quality based on the user's voice input. As a result, the advice unit can provide health advice in an interactive manner, allowing the user to manage their health more effectively.

[0069] The data collection unit can estimate the user's emotion and adjust the timing of data collection based on the emotion data. For example, the data collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on the change in facial expression. The data collection unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the data collection unit analyzes the tone and speed of the voice and calculates the emotion score. The data collection unit can also 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 data collection unit calculates the emotion score based on the fluctuation of the heart rate. This allows the data collection unit to adjust the timing of data collection according to the user's emotion. For example, if the user is feeling stressed, the frequency of data collection is reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the data collection unit to reduce the burden on the user by adjusting the timing of data collection according to the user's emotions.

[0070] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral data. The past behavioral data includes, for example, data from the past year, specific event data, etc., but is not limited to such examples. The data collection unit, for example, analyzes the past behavioral data using AI. The AI ​​selects the optimal data collection method based on the user's past behavioral data. For example, the data collection unit proposes a method of automatically collecting data from a wearable device based on data manually input by the user in the past. The data collection unit can also preferentially collect data from a device that the user has frequently used in the past. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. Thereby, the data collection unit can select the optimal data collection method by analyzing the past behavioral data. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the past behavioral data to the generation AI and cause the generation AI to select the optimal data collection method.

[0071] The data collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. The data collection unit, for example, filters data taking into consideration the user's current living situation. The living situation includes, for example, whether or not the user has a job, the home environment, and the like, but is not limited to such examples. For example, if the user is working, the data collection unit preferentially collects data related to the work. In addition, if the user is interested in the home environment, the data collection unit can also preferentially collect data related to the home environment. Furthermore, the data collection unit filters data based on the user's field of interest. The field of interest includes, for example, hobbies, topics of interest, and the like, but is not limited to such examples. For example, if the user is on a diet, the data collection unit preferentially collects data related to the contents of meals. In addition, if the user is interested in exercise, the data collection unit can also preferentially collect data related to the frequency and type of exercise. In addition, if the user wants to improve the quality of sleep, the data collection unit can also preferentially collect data related to sleep time and quality. In this way, the data collection unit can collect more relevant data by filtering data based on the user's living situation and field of interest. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the emotion data. For example, the data collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expression. The data collection unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the data collection unit analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also 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 data collection unit calculates an emotion score based on heart rate fluctuations. This allows the data collection unit to determine the priority of data to be collected based on the user's emotions. For example, if the user is feeling stressed, data related to stress is preferentially collected. Also, if the user is relaxed, overall health data can be collected in a balanced manner. Furthermore, if the user is in a hurry, only important data can be preferentially collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the data collection unit to prioritize data based on the user's emotions, thereby allowing important data to be collected preferentially.

[0073] The data collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. The data collection unit collects data, for example, taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is traveling, the data collection unit preferentially collects data related to meals and exercise at the travel destination. In addition, when the user is at home, the data collection unit can also preferentially collect data related to daily life. Furthermore, when the user is at work, the data collection unit can also preferentially collect data related to the work environment. Thereby, the data collection unit can collect highly relevant data preferentially by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant data.

[0074] The data collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The data collection unit, for example, analyzes the social media activity of the user. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The data collection unit, for example, uses AI to analyze the social media activity. The AI ​​collects related data based on the social media activity of the user. For example, the data collection unit collects data on meal contents based on photos of meals shared by the user on social media. The data collection unit can also collect data on the frequency and type of exercise based on the records of exercise shared by the user on social media. Furthermore, the data collection unit can collect data on sleep time and quality based on the records of sleep shared by the user on social media. In this way, the data collection unit can collect related data by analyzing the social media activity. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the social media activity of the user to the generation AI and cause the generation AI to collect related data.

[0075] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the emotion data. 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 analysis unit calculates an emotion score based on the change in facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also 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 analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the expression method of the analysis based on the user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result that covers the main points can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the analysis unit to provide an analysis result that is easy for the user to understand by adjusting the way the analysis is expressed based on the user's emotions.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data, for example. The importance of the behavioral data includes, for example, the level of health risk, frequency, etc., but is not limited to such examples. The analysis unit evaluates the importance of the behavioral data, for example, using AI. The AI ​​adjusts the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data. In addition, the analysis unit can perform a simplified analysis on behavioral data with low importance. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the behavioral data. As a result, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. The analysis unit applies different analysis algorithms depending on the category of behavioral data, for example. The categories of behavioral data include, but are not limited to, exercise data, dietary data, and sleep data. The analysis unit classifies the category of behavioral data using, for example, AI. The AI ​​applies an appropriate analysis algorithm depending on the category of behavioral data. For example, the analysis unit applies a nutrient analysis algorithm to dietary data. In addition, the analysis unit can apply an exercise intensity analysis algorithm to exercise data. Furthermore, the analysis unit can also apply a sleep quality analysis algorithm to sleep data. This allows the analysis unit to apply different analysis algorithms depending on the category of behavioral data, thereby enabling more accurate analysis. 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 category of behavioral data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotion data. 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 analysis unit calculates an emotion score based on the change in facial expression. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also 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 analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. For example, if the user is nervous, a short and to the point analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the analysis unit to provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions.

[0079] The analysis unit can determine the priority of the analysis based on the collection time of the behavioral data during the analysis. The analysis unit determines the priority of the analysis based on, for example, the collection time of the behavioral data. The collection time of the behavioral data includes, for example, recent data, past data, and the like, but is not limited to such examples. The analysis unit evaluates the collection time of the behavioral data using, for example, AI. The AI ​​determines the priority of the analysis based on the collection time of the behavioral data. For example, the analysis unit preferentially analyzes the recently collected data. In addition, the analysis unit can analyze an overall trend based on long-term data. Furthermore, the analysis unit can also analyze a trend for each period based on data collected in a specific period. Thereby, the analysis unit can preferentially analyze the latest data by determining the priority of the analysis based on the collection time of the behavioral data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the behavioral data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the behavioral data. The relevance of the behavioral data includes, for example, correlation and causal relationship, but is not limited to such examples. The analysis unit, for example, uses AI to evaluate the relevance of the behavioral data. The AI ​​adjusts the order of analysis based on the relevance of the behavioral data. For example, the analysis unit preferentially analyzes data with high relevance. In addition, the analysis unit can analyze data with low relevance later. Furthermore, the analysis unit can also determine the order of analysis according to the relevance of the data. As a result, the analysis unit adjusts the order of analysis based on the relevance of the behavioral data, thereby enabling efficient analysis. 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 the behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0081] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the emotion data. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the change in facial expression. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice and calculates an emotion score. The prediction unit can also 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 prediction unit calculates an emotion score based on the fluctuation of the heart rate. This allows the prediction unit to adjust the prediction criteria based on the user's emotions. For example, if the user is nervous, high-risk diseases are predicted preferentially. Also, if the user is relaxed, the overall health risk can be predicted in a balanced manner. Furthermore, if the user is in a hurry, only important prediction results can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the prediction unit to provide a prediction result appropriate for the user by adjusting the prediction criteria based on the user's emotions.

[0082] The prediction unit can improve the accuracy of prediction by considering the interrelationship of behavioral data at the time of prediction. The prediction unit, for example, makes a prediction by considering the interrelationship of behavioral data. The interrelationship of behavioral data includes, for example, the relationship between exercise and food, the relationship between sleep and stress, and the like, but is not limited to such examples. The prediction unit, for example, uses AI to evaluate the interrelationship of behavioral data. The AI ​​improves the accuracy of prediction based on the interrelationship of behavioral data. For example, the prediction unit predicts the risk of disease by considering the interrelationship between food data and exercise data. In addition, the prediction unit can predict the health risk by considering the interrelationship between sleep data and stress data. Furthermore, the prediction unit can predict the overall health risk by considering the interrelationship of all behavioral data. Thereby, the prediction unit can improve the accuracy of prediction by considering the interrelationship of behavioral data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the interrelationship of behavioral data to the generation AI and cause the generation AI to execute the improvement of the accuracy of prediction.

[0083] The prediction unit can make a prediction by taking into consideration the attribute information of the submitter of the behavioral data at the time of prediction. The prediction unit makes a prediction by taking into consideration the attribute information of the submitter of the behavioral data, for example. The attribute information of the submitter includes, for example, age, gender, occupation, etc., but is not limited to such examples. The prediction unit evaluates the attribute information of the submitter using, for example, AI. The AI ​​makes a prediction based on the attribute information of the submitter. For example, the prediction unit predicts a disease risk according to the age by taking into consideration the age of the user. In addition, the prediction unit can predict a disease risk according to the gender by taking into consideration the gender of the user. Furthermore, the prediction unit can predict a health risk according to the occupation by taking into consideration the occupation of the user. This enables the prediction unit to make a more individualized prediction by taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the attribute information of the submitter to the generation AI and cause the generation AI to execute the prediction.

[0084] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the emotion data. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on changes in facial expression. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the prediction unit analyzes the tone and speed of the voice and calculates an emotion score. The prediction unit can also 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 prediction unit calculates an emotion score based on heart rate fluctuations. This allows the prediction unit to adjust the order in which prediction results are displayed based on the user's emotions. For example, if the user is nervous, important prediction results are displayed first. Also, if the user is relaxed, the overall prediction results can be displayed in a balanced manner. Furthermore, if the user is in a hurry, prediction results that capture the main points can be displayed first. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. As a result, the prediction unit can provide information that is important to the user with priority by adjusting the display order of the prediction results based on the user's emotions.

[0085] The prediction unit can make a prediction taking into account the geographical distribution of the behavioral data at the time of prediction. The prediction unit makes a prediction taking into account, for example, the geographical distribution of the behavioral data. The geographical distribution includes, for example, health risks for each region, geographical clusters, and the like, but is not limited to such examples. The prediction unit, for example, evaluates the geographical distribution using AI. The AI ​​makes a prediction based on the geographical distribution. For example, the prediction unit makes a prediction taking into account the incidence rate of a disease in the region where the user lives. The prediction unit can also predict the health risk of a travel destination where the user travels. Furthermore, the prediction unit can also predict the health risk for each region based on the user's range of movement. As a result, the prediction unit can predict the health risk for each region by taking the geographical distribution into account. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data of the geographical distribution to the generation AI and cause the generation AI to execute the prediction.

[0086] The prediction unit can improve the accuracy of the prediction by referring to related literature of the behavioral data at the time of prediction. The prediction unit, for example, makes a prediction by referring to related literature of the behavioral data. The related literature includes, for example, academic papers, technical reports, etc., but is not limited to such examples. The prediction unit, for example, evaluates the related literature using AI. The AI ​​improves the accuracy of the prediction based on the related literature. For example, the prediction unit predicts the risk of disease by referring to the latest medical research. In addition, the prediction unit can predict health risks based on past research data. Furthermore, the prediction unit can also improve the reliability of the prediction result by referring to related literature. Thereby, the prediction unit can improve the accuracy of the prediction by referring to the related literature. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input data of related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the prediction.

[0087] The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the emotion data. For example, the advice unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the advice unit calculates an emotion score based on the change in the facial expression. The advice unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the advice unit analyzes the tone and speed of the voice and calculates an emotion score. The advice unit can also 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 advice unit calculates an emotion score based on the fluctuation of the heart rate. This allows the advice unit to adjust the way of expressing advice based on the user's emotion. For example, if the user is nervous, advice can be provided in a calm expression. Also, detailed advice can be provided if the user is relaxed. Furthermore, concise advice that is to the point can be provided if the user is in a hurry. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the advice unit to provide advice that is easy for the user to understand by adjusting the way the advice is expressed based on the user's emotions.

[0088] The advice unit can adjust the level of detail of the advice based on the importance of the prediction result when providing the advice. The advice unit adjusts the level of detail of the advice based on the importance of the prediction result, for example. The importance of the prediction result includes, but is not limited to, the level of risk and the degree of impact. The advice unit, for example, evaluates the importance of the prediction result using an AI. The AI ​​adjusts the level of detail of the advice based on the importance of the prediction result. For example, the advice unit provides detailed advice for an important prediction result. In addition, for a prediction result with low importance, simplified advice can be provided. Furthermore, the advice unit can also determine the priority of advice according to the importance of the prediction result. As a result, the advice unit adjusts the level of detail of the advice based on the importance of the prediction result, thereby enabling efficient advice. Part or all of the above-mentioned processing in the advice unit may be performed using an AI, for example, or may be performed without using an AI. For example, the advice unit can input the importance of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0089] The advice unit can apply different advice algorithms according to the category of the prediction result when providing advice. The advice unit applies different advice algorithms according to the category of the prediction result, for example. The category of the prediction result includes, but is not limited to, for example, the type of disease and the type of risk. The advice unit classifies the category of the prediction result using, for example, AI. The AI ​​applies an appropriate advice algorithm according to the category of the prediction result. For example, the advice unit applies a nutrition advice algorithm to the prediction result regarding diet. In addition, the advice unit can apply an exercise advice algorithm to the prediction result regarding exercise. Furthermore, the advice unit can also apply a sleep advice algorithm to the prediction result regarding sleep. As a result, the advice unit can apply different advice algorithms according to the category of the prediction result, thereby making it possible to provide more accurate advice. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the category of the prediction result to the generation AI and cause the generation AI to apply an appropriate advice algorithm.

[0090] The advice unit can estimate the user's emotion and adjust the length of the advice based on the emotion data. For example, the advice unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the advice unit calculates an emotion score based on the change in facial expression. The advice unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the advice unit analyzes the tone and speed of the voice and calculates an emotion score. The advice unit can also 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 advice unit calculates an emotion score based on the fluctuation of the heart rate. This allows the advice unit to adjust the length of the advice based on the user's emotion. For example, if the user is nervous, short and to the point advice can be provided. Also, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the advice unit to provide advice of an appropriate length for the user by adjusting the length of the advice based on the user's emotions.

[0091] The advice unit can determine the priority of advice based on the submission time of the prediction result when providing advice. The advice unit determines the priority of advice based on, for example, the submission time of the prediction result. The submission time of the prediction result includes, for example, a recent prediction result, a past prediction result, and the like, but is not limited to such examples. The advice unit evaluates the submission time of the prediction result using, for example, an AI. The AI ​​determines the priority of advice based on the submission time of the prediction result. For example, the advice unit provides advice preferentially based on the recent prediction result. In addition, the advice unit can provide overall advice based on a long-term prediction result. Furthermore, the advice unit can provide advice for each period based on the prediction result submitted in a specific period. Thereby, the advice unit can provide the latest information preferentially by determining the priority of advice based on the submission time of the prediction result. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the submission time of the prediction result to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0092] The advice unit can adjust the order of advice based on the relevance of the prediction result when providing advice. The advice unit adjusts the order of advice based on, for example, the relevance of the prediction result. The relevance of the prediction result includes, for example, correlation, causal relationship, and the like, but is not limited to such examples. The advice unit evaluates the relevance of the prediction result using, for example, an AI. The AI ​​adjusts the order of advice based on the relevance of the prediction result. For example, the advice unit provides advice preferentially based on a highly relevant prediction result. In addition, the advice unit can provide advice later based on a less relevant prediction result. Furthermore, the advice unit can also determine the order of advice according to the relevance of the prediction result. As a result, the advice unit adjusts the order of advice based on the relevance of the prediction result, thereby enabling efficient advice. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the relevance of the prediction result to the generation AI and cause the generation AI to adjust the order of advice.

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

[0094] The health management system can also collect social network data of the user and analyze the data in the analysis unit. For example, the frequency and content of interactions with the user's friends and family can be analyzed to evaluate the presence or absence of social support. This can provide advice to increase social activity if the user is isolated. Also, if the user is feeling stressed, it can recommend interactions with trusted friends and family. Furthermore, health risks can be predicted based on the user's social network data. For example, if the health of friends and family is deteriorating, the user's own health risk may also increase, so advice can be given to take early measures.

[0095] The data collection unit can also collect purchase history data of the user and analyze the data with the analysis unit. For example, data on foods and supplements purchased by the user can be collected to evaluate the nutritional balance. This can be used to advise the user to reduce their intake if they are consuming too much of a particular nutrient. Also, if the user has purchased healthy foods, it can be recommended that they continue that behavior. Furthermore, health risks can be predicted based on the user's purchase history data. For example, if the user frequently purchases high-calorie foods, it can be recommended that they improve their diet because they are at higher risk of obesity and diabetes.

[0096] The advice unit can estimate the user's emotions and adjust the timing of advice based on the emotion data. For example, if the user is feeling stressed, advice to relax can be provided. Also, if the user is relaxed, advice to maintain a healthy lifestyle can be provided. Furthermore, if the user is in a hurry, concise advice that is to the point can be provided. In this way, the advice unit can provide advice at the optimal timing for the user by adjusting the timing of advice based on the user's emotions.

[0097] The data collection unit can estimate the user's emotions and adjust the method of data collection based on the emotion data. For example, if the user is stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. In this way, the data collection unit can reduce the burden on the user by adjusting the method of data collection according to the user's emotions.

[0098] The data collection unit can analyze the user's past behavioral data and select the optimal timing for data collection. For example, the time period in which the user input data in the past is analyzed, and data collection is performed during that time period. In addition, the data collection unit can analyze the days of the week and dates on which the user found it easy to input data in the past, and collect data on those days of the week and dates. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most efficient timing for data collection. In this way, the data collection unit can select the optimal timing for data collection by analyzing past behavioral data.

[0099] The data collection unit can adjust the method of data collection based on the user's current living situation and areas of interest. For example, if the user is working, data related to work can be collected preferentially. Also, if the user is interested in the home environment, data related to the home environment can be collected preferentially. Furthermore, if the user is on a diet, data on the contents of meals can be collected preferentially. In this way, the data collection unit can collect more relevant data by adjusting the method of data collection based on the user's living situation and areas of interest.

[0100] The data collection unit can estimate the user's emotions and determine the type of data to be collected based on the emotion data. For example, when the user is feeling stressed, data related to stress can be collected with priority. When the user is relaxed, overall health data can be collected in a balanced manner. When the user is in a hurry, only important data can be collected with priority. Thus, the data collection unit can collect important data with priority by determining the type of data to be collected based on the user's emotions.

[0101] The data collection unit can adjust the method of data collection based on the geographical location information of the user. For example, when the user is traveling, data related to meals and exercise at the travel destination can be collected with priority. When the user is at home, data related to daily life can be collected with priority. Furthermore, when the user is at work, data related to the work environment can be collected with priority. In this way, the data collection unit can collect highly relevant data by adjusting the method of data collection based on the geographical location information of the user.

[0102] The data collection unit can analyze the user's social media activity and collect related data. For example, data on meal contents can be collected based on photos of meals shared by the user on social media. Data on the frequency and type of exercise can also be collected based on exercise records shared by the user on social media. Furthermore, data on sleep duration and quality can also be collected based on sleep records shared by the user on social media. In this way, the data collection unit can collect related data by analyzing social media activity.

[0103] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotion data. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result that focuses on the main points can be provided. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the way the analysis is presented based on the user's emotions.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The data collection unit collects user behavior data. The user behavior data includes food content, exercise frequency, sleep time, etc. The data collection unit collects data manually entered by the user and data automatically collected from wearable devices and mobile devices. For example, it collects heart rate and step count data from wearable devices and collects food photos and exercise records from mobile devices. Step 2: The analysis unit analyzes the data collected by the data collection unit and learns behavioral patterns. The analysis unit uses AI to analyze the data and learns behavioral patterns based on the user's behavioral data. For example, it learns patterns for predicting health risks from the user's diet and exercise frequency. Step 3: The prediction unit predicts what diseases the user is likely to develop, when they will develop, and the cure rate based on the behavioral patterns learned by the analysis unit. The prediction unit uses AI to make predictions and predicts the risk of diabetes and heart disease based on the user's behavioral patterns. Step 4: The advice unit provides daily health advice based on the results predicted by the prediction unit. The advice unit uses AI to provide advice on improving diet, exercise, and sleep quality based on the user's behavioral data and prediction results.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<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.

[0108] 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.

[0109] Each of the multiple elements including the data collection unit, analysis unit, prediction unit, and advice unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects behavioral data of the user using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn behavioral patterns. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the risk of disease and the onset time based on the learned behavioral patterns. The advice unit is realized, for example, by the control unit 46A of the smart device 14, and provides health advice to the user based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0115] 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).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0117] 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0120] 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0125] Each of the multiple elements including the data collection unit, analysis unit, prediction unit, and advice unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn behavior patterns. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the risk of disease and the onset time based on the learned behavior patterns. The advice unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides health advice to the user based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] 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).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0136] 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.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0141] Each of the multiple elements including the data collection unit, analysis unit, prediction unit, and advice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn behavior patterns. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the risk of disease and the onset time based on the learned behavior patterns. The advice unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides health advice to the user based on the prediction results. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] 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).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] 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.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0153] 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.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may 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.

[0158] Each of the multiple elements including the data collection unit, analysis unit, prediction unit, and advice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects behavioral data of the user using the camera 42 and microphone 238 of the robot 414, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn behavioral patterns. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the risk of disease and the onset time based on the learned behavioral patterns. The advice unit is realized, for example, by the control unit 46A of the robot 414, and provides health advice to the user based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a 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.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Appendix 1) A data collection unit that collects user behavior data; an analysis unit that analyzes the data collected by the data collection unit and learns behavioral patterns; A prediction unit that predicts a likely disease, its onset time, and a cure rate based on the behavioral pattern learned by the analysis unit; and an advice unit that provides daily health advice based on the results predicted by the prediction unit. A system characterized by: (Appendix 2) The data collection unit includes: Collect data on the user's diet, exercise frequency, and sleep duration 2. The system of claim 1, (Appendix 3) The advice unit, Providing interactive health advice 2. The system of claim 1, (Appendix 4) The data collection unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 5) The data collection unit includes: Analyze users' past behavioral data and select the appropriate data collection method 2. The system of claim 1, (Appendix 6) The data collection unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1, (Appendix 7) The data collection unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 8) The data collection unit includes: When collecting data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 9) The data collection unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, adjust the level of detail based on the importance of behavioral data 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of behavioral data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on when behavioral data was collected 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the order of analysis based on the relevance of behavioral data 2. The system of claim 1, (Appendix 16) The prediction unit is Infer user sentiment and adjust prediction criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The prediction unit is When making predictions, we take into account correlations in behavioral data to improve the accuracy of predictions. 2. The system of claim 1, (Appendix 18) The prediction unit is When making predictions, the attribute information of the person who submitted the behavioral data is taken into consideration. 2. The system of claim 1, (Appendix 19) The prediction unit is Infer user sentiment and adjust the order in which prediction results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The prediction unit is When making predictions, the geographic distribution of behavioral data is taken into account 2. The system of claim 1, (Appendix 21) The prediction unit is When making predictions, refer to relevant literature on behavioral data to improve the accuracy of predictions. 2. The system of claim 1, (Appendix 22) The advice unit, Infer the user's emotions and adjust the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The advice unit, When providing advice, adjust the level of detail of the advice based on the importance of the prediction results 2. The system of claim 1, (Appendix 24) The advice unit, When providing advice, different advice algorithms are applied depending on the category of the predicted result. 2. The system of claim 1, (Appendix 25) The advice unit, Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The advice unit, When providing advice, the priority of advice is determined based on the time of submission of prediction results. 2. The system of claim 1, (Appendix 27) The advice unit, When providing advice, adjust the order of advice based on the relevance of the predicted results 2. The system of claim 1, [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects user behavior data; an analysis unit that analyzes the data collected by the data collection unit and learns behavioral patterns; A prediction unit that predicts a likely disease, its onset time, and a cure rate based on the behavioral pattern learned by the analysis unit; and an advice unit that provides daily health advice based on the results predicted by the prediction unit. A system characterized by:

2. The data collection unit includes: Collect data on the user's diet, exercise frequency, and sleep duration 2. The system of claim 1 .

3. The advice unit is Providing interactive health advice 2. The system of claim 1 .

4. The data collection unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

5. The data collection unit includes: Analyze users' past behavioral data and select the appropriate data collection method 2. The system of claim 1 .

6. The data collection unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1 .

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

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

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