system

Abstract

The system according to the embodiment aims to predict health risks based on individual health information and recommend appropriate countermeasure plans. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a tracing unit. The collection unit collects health information. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends an action plan based on the analysis results obtained by the analysis unit. The tracing unit traces the execution status of the plan recommended by the recommendation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately predict health risks or recommend treatment plans based on individual health information, and there is room for improvement.

[0005] The system according to the embodiment aims to predict health risks based on individual health information and recommend appropriate countermeasure plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a tracing unit. The collection unit collects health information. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends a response plan based on the analysis results obtained by the analysis unit. The tracing unit traces the execution status of the plan recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict health risks based on individual health information and recommend appropriate countermeasure plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention links each individual's health information (e.g., medical information, activity level information), predicts potential health risks, recommends countermeasure plans, and tracks their implementation. This health management system collects each individual's health information and uses AI to analyze it. Based on the analysis results, the system then predicts individual health risks and recommends countermeasure plans. For example, it may recommend an appropriate exercise plan for someone who is not getting enough exercise, or a balanced meal plan for someone with an unbalanced diet. Furthermore, it tracks the implementation status of the recommended plan and modifies it as necessary. This allows the health management system to optimize each individual's health status and realize a healthy society.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a tracing unit. The collection unit collects health information for each individual. The health information includes, but is not limited to, medical information, activity level information, and dietary information. The collection unit can collect health information by, for example, acquiring data from a wearable device, manually entering the information, or collecting sensor data. The analysis unit analyzes the health information collected by the collection unit. The analysis unit analyzes the health information using, for example, statistical analysis or a machine learning algorithm, and predicts individual health risks. The analysis unit can predict health risks such as cardiovascular risk, diabetes risk, and obesity risk. The recommendation unit recommends an action plan based on the analysis results obtained by the analysis unit. The recommendation unit can recommend action plans such as an exercise plan, a meal plan, and a medical plan. The recommendation unit can make recommendations based on the user's past data or an algorithm. The tracing unit traces the implementation status of the plan recommended by the recommendation unit. The tracing unit can monitor the execution status of the plan by, for example, acquiring data from devices or by users' self-reporting, and can modify the plan as necessary. This enables the health management system according to the embodiment to link the health information of each individual, predict health risks, recommend countermeasure plans, and trace execution.

[0030] The collection unit can collect at least one of health information, including medical information and activity amount information. The collection unit, for example, collects medical information. Medical information includes, but is not limited to, diagnostic results, prescriptions, and medical history. For example, the collection unit can acquire diagnostic results from an electronic medical record and prescription information from a pharmacy system. The collection unit can also collect activity amount information. Activity amount information includes, but is not limited to, the number of steps, exercise time, and calories burned. For example, the collection unit can acquire step count data from a wearable device and exercise time from a fitness app. In this way, the collection unit can collect medical information and activity amount information, thereby enabling the collection of comprehensive health information.

[0031] The analysis unit can analyze the collected health information and predict individual health risks. The analysis unit, for example, performs statistical analysis of the collected health information. Examples of statistical analysis include, but are not limited to, regression analysis, analysis of variance, and clustering. The analysis unit can, for example, use regression analysis to build a health risk prediction model and use analysis of variance to identify health risk factors. The analysis unit can also analyze the health information using machine learning algorithms. Examples of machine learning algorithms include, but are not limited to, decision trees, random forests, and neural networks. The analysis unit can, for example, use decision trees to build a health risk classification model and use random forests to improve the accuracy of health risk prediction. As a result, the analysis unit analyzes the health information and predicts individual health risks, enabling appropriate measures to be taken.

[0032] The recommendation unit can recommend a countermeasure plan based on the analysis results. The recommendation unit, for example, recommends an exercise plan based on the analysis results. Exercise plans include, for example, walking, jogging, and strength training, but are not limited to these examples. The recommendation unit can, for example, propose a walking plan and recommend a jogging plan. The recommendation unit can also recommend a meal plan based on the analysis results. Meal plans include, for example, a balanced diet, a low-calorie diet, and nutritional supplements, but are not limited to these examples. The recommendation unit can, for example, propose a balanced meal plan and recommend a low-calorie meal plan. As a result, when the recommendation unit recommends a countermeasure plan based on the analysis results, a plan tailored to each individual's health condition is provided.

[0033] The tracing unit can monitor the implementation status of the recommended plan and modify the plan as necessary. The tracing unit monitors the implementation status of the recommended plan, for example, by acquiring data from a device. Examples of devices include, but are not limited to, a wearable device, a smartphone, a fitness app, etc. The tracing unit can, for example, acquire data from a wearable device and collect data from a smartphone. The tracing unit can also monitor the implementation status of the plan through self-reports by the user. Examples of self-reports include, but are not limited to, diary-style reports, questionnaire-style reports, and reports through an app. The tracing unit can, for example, receive diary-style reports and collect questionnaire-style reports. This enables effective health management by the tracing unit monitoring the implementation status of the plan and modifying the plan as necessary.

[0034] The collection unit can analyze the user's past health information collection history and select an appropriate collection method. For example, the collection unit acquires the user's past health information collection history from a database and analyzes it. The database includes, for example, past collection methods, collection frequencies, collection results, etc., but is not limited to these examples. For example, the collection unit can acquire past collection methods from the database and analyze collection frequencies. The collection unit can also select an optimal collection method from the past collection history. Collection methods include, for example, wearable devices, manual input, sensor data, etc., but are not limited to these examples. For example, the collection unit can select a collection method using a wearable device and recommend manual input. In this way, the collection unit can select an optimal collection method by analyzing the past collection history.

[0035] The collection unit may filter the health information based on the user's current living situation and areas of interest when collecting the health information. The collection unit may, for example, collect the user's current living situation through a questionnaire or interview. Examples of living situations include, but are not limited to, occupation, home environment, and daily activity patterns. For example, the collection unit may collect information about the user's occupation through a questionnaire and information about the home environment through an interview. The collection unit may also investigate the user's areas of interest and filter the health information when collecting the health information. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, and health goals. For example, the collection unit may collect information about hobbies through a questionnaire and information about health goals through an interview. In this way, the collection unit may filter the health information based on the user's living situation and areas of interest, thereby collecting highly relevant information.

[0036] When collecting health information, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. The collection unit, for example, acquires the user's geographical location information using GPS data or address information. Examples of geographical location information include, but are not limited to, a current location, a movement history, and a specific region. For example, the collection unit can acquire the user's current location using GPS data and identify a specific region using address information. The collection unit can also prioritize collection of highly relevant health information based on the geographical location information. Examples of highly relevant information include, but are not limited to, region-specific health risks, local medical facility information, and local health event information. For example, the collection unit can prioritize collection of region-specific health risk information and acquire local medical facility information. In this way, the collection unit can collect region-specific health information by taking the geographical location information into consideration.

[0037] When collecting health information, the collection unit may analyze the user's social media activity and collect related information. For example, the collection unit may analyze the user's social media activity using data mining technology. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may analyze the content of posts and count the number of likes. The collection unit may also collect related health information based on the social media activity. For example, related information may include, but is not limited to, health topics of interest to the user, health information shared by followers, and health information currently trending on social media. For example, the collection unit may identify health topics of interest to the user and collect health information shared by followers. In this way, the collection unit may analyze the social media activity to collect health information of interest.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information. For example, the analysis unit evaluates the importance of the health information using an evaluation by a medical professional or statistical data. Examples of importance include, but are not limited to, the severity of the illness, the urgency of the symptoms, and the need for treatment. For example, the analysis unit can evaluate the severity of the illness based on an evaluation by a medical professional and the urgency of the symptoms using statistical data. The analysis unit can also adjust the level of detail of the analysis based on the importance. Examples of the level of detail include, but are not limited to, a detailed analysis, a simplified analysis, and an analysis that focuses on the main points. For example, the analysis unit can perform a detailed analysis of important health information and a simplified analysis of general health information. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information, thereby performing a detailed analysis of important information.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health information. For example, the analysis unit classifies the categories of health information based on the type of disease or the type of symptom. Examples of categories include, but are not limited to, cardiovascular disease, diabetes, and obesity. For example, the analysis unit can identify information related to cardiovascular disease and classify information related to diabetes. The analysis unit can also apply different analysis algorithms depending on the category. Examples of analysis algorithms include, but are not limited to, specialized analysis algorithms, exercise analysis algorithms, and nutrition analysis algorithms. For example, the analysis unit can apply a specialized analysis algorithm to cardiovascular disease and an exercise analysis algorithm to activity amount information. In this way, the analysis unit can apply analysis algorithms depending on the category of health information, thereby enabling more accurate analysis.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the health information. The analysis unit, for example, obtains the collection time of the health information from a database and determines the priority of analysis. The collection time includes, for example, but is not limited to, recently collected information, information collected in the past, and information collected periodically. The analysis unit can, for example, prioritize analyzing recently collected information and refer to information collected in the past. The analysis unit can also adjust the priority of analysis based on the collection time. The priority includes, for example, prioritizing the most recent information, referring to past information, or prioritizing periodic information, but is not limited to, the above. The analysis unit can, for example, prioritize analyzing the most recent information and refer to past information. In this way, the analysis unit can prioritize analysis of the most recent information by determining the priority of analysis based on the collection time of the health information, allowing the most recent information to be analyzed preferentially.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health information. The analysis unit, for example, evaluates the relevance of the health information using co-occurrence analysis or correlation analysis. Relevance includes, but is not limited to, common factors, correlations, and causal relationships. For example, the analysis unit can identify common factors using co-occurrence analysis and evaluate correlations using correlation analysis. The analysis unit can also adjust the order of analysis based on the relevance. For example, the order can include, but is not limited to, prioritizing highly relevant information, postponing less relevant information, or grouping related information. For example, the analysis unit can prioritize analyzing highly relevant information and postponing less relevant information. As a result, by adjusting the order of analysis based on the relevance of the health information, highly relevant information can be prioritized for analysis.

[0042] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the health risk. The recommendation unit, for example, evaluates the importance of the health risk using an evaluation by a medical professional or statistical data. The importance includes, for example, the severity of the disease, the urgency of the symptoms, and the need for treatment, but is not limited to these examples. The recommendation unit can, for example, evaluate the severity of the disease based on the evaluation by a medical professional and evaluate the urgency of the symptoms using statistical data. The recommendation unit can also adjust the level of detail of the recommendation based on the importance. The level of detail includes, for example, a detailed recommendation, a simplified recommendation, a recommendation that focuses on the main points, but is not limited to these examples. The recommendation unit can, for example, provide a detailed recommendation for an important health risk and a simplified recommendation for a general health risk. In this way, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the health risk, thereby making a detailed recommendation for an important risk.

[0043] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the health risk category. For example, the recommendation unit classifies health risk categories based on the type of disease or the type of symptom. Examples of categories include, but are not limited to, cardiovascular disease, diabetes, and obesity. For example, the recommendation unit can identify information related to cardiovascular disease and classify information related to diabetes. The recommendation unit can also apply different recommendation algorithms depending on the category. Examples of recommendation algorithms include, but are not limited to, an algorithm for recommending an exercise plan, an algorithm for recommending a meal plan, and an algorithm for recommending a relaxation plan. For example, the recommendation unit can apply an algorithm for recommending an exercise plan for lack of exercise, and an algorithm for recommending a meal plan for unbalanced diet. In this way, the recommendation unit can apply a recommendation algorithm according to the health risk category, thereby making more accurate recommendations.

[0044] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of occurrence of the health risk. The recommendation unit, for example, obtains the time of occurrence of the health risk from a database and determines the priority of the recommendation. The time of occurrence includes, for example, but is not limited to, the most recent occurrence, past occurrence, and periodic occurrence. The recommendation unit can, for example, prioritize the most recent occurrence and refer to past occurrences. The recommendation unit can also adjust the priority of the recommendation based on the time of occurrence. The priority includes, for example, prioritizing the most recent risk, referring to past risks, and emphasizing periodic risks, but is not limited to, the example. The recommendation unit can, for example, prioritize the most recent risk and refer to past risks. In this way, the recommendation unit can determine the priority of the recommendation based on the time of occurrence of the health risk, thereby enabling a prompt response to the most recent risk.

[0045] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of health risks. The recommendation unit, for example, evaluates the relevance of health risks using co-occurrence analysis or correlation analysis. Relevance includes, for example, common factors, correlations, and causal relationships, but is not limited to these examples. The recommendation unit can, for example, identify common factors using co-occurrence analysis and evaluate correlations using correlation analysis. The recommendation unit can also adjust the order of recommendations based on the relevance. The order includes, for example, prioritizing highly relevant risks, postponing less relevant risks, and grouping related risks, but is not limited to these examples. The recommendation unit can, for example, prioritize highly relevant risks and postpone less relevant risks. In this way, by the recommendation unit adjusting the order of recommendations based on the relevance of health risks, highly relevant risks can be addressed preferentially.

[0046] During tracing, the tracing unit can analyze the user's past plan execution history and select the optimal tracing method. The tracing unit, for example, acquires the user's past plan execution history from a database and analyzes it. The execution history includes, for example, past plan execution status, success rate, execution frequency, etc., but is not limited to these examples. The tracing unit can, for example, acquire the past plan execution status from the database and analyze the success rate. The tracing unit can also select the optimal tracing method from the past execution history. The tracing method includes, for example, a simple tracing method, a detailed tracing method, a tracing method that focuses on the main points, etc., but is not limited to these examples. The tracing unit can, for example, preferentially suggest plan execution methods that have been successful in the past and optimize the tracing frequency based on the past execution history. In this way, the tracing unit can select the optimal tracing method by analyzing the past plan execution history.

[0047] The tracing unit can customize the tracing means based on the user's current living situation during tracing. The tracing unit, for example, collects the user's current living situation through a questionnaire or interview. The living situation includes, but is not limited to, for example, occupation, home environment, and daily activity patterns. For example, the tracing unit can collect information about the user's occupation through a questionnaire and information about the home environment through an interview. The tracing unit can also customize the tracing means based on the living situation. For example, the tracing means can include, but are not limited to, a simple tracing means, a detailed tracing means, and a tracing means that focuses on the main points. For example, the tracing unit can prioritize relevant tracing means based on the current living situation and flexibly change the tracing means depending on the living situation. This allows the tracing unit to customize the tracing means based on the user's living situation, thereby enabling more effective tracing.

[0048] During tracing, the tracing unit can select an appropriate tracing method based on the user's geographical location information. The tracing unit, for example, acquires the user's geographical location information using GPS data or address information. Examples of geographical location information include, but are not limited to, a current location, a movement history, and a specific region. For example, the tracing unit can acquire the user's current location using GPS data and identify a specific region using address information. The tracing unit can also select an appropriate tracing method based on the geographical location information. Examples of tracing methods include, but are not limited to, a region-specific tracing method, a general tracing method, a customized tracing method, and the like. For example, the tracing unit can preferentially provide tracing methods related to a specific region and select a region-specific tracing method. This allows the tracing unit to select a region-specific tracing method by taking the geographical location information into consideration.

[0049] During tracing, the tracing unit can analyze the user's social media activity and suggest tracing methods. The tracing unit, for example, analyzes the user's social media activity using data mining technology. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The tracing unit can, for example, analyze the content of posts and count the number of likes. The tracing unit can also suggest tracing methods based on the social media activity. Tracing methods include, for example, but are not limited to, simple tracing methods, detailed tracing methods, and key tracing methods. The tracing unit can, for example, suggest tracing methods of interest from the social media activity and provide related tracing methods preferentially. In this way, the tracing unit can suggest tracing methods of interest by analyzing the social media activity.

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

[0051] When analyzing collected health information, the analysis unit can compare it with the user's past health data to detect abnormal values. For example, it can compare the user's past blood pressure data with current data and issue a warning if there are abnormal fluctuations. It can also compare past exercise data with current data and warn the user if there is a sudden change in the amount of exercise. Furthermore, the analysis unit can compare past dietary data with current data and suggest improvements if there is an imbalance in nutritional balance. In this way, by detecting abnormal values ​​by comparing with past data, the analysis unit can discover health risks early and take appropriate measures.

[0052] When monitoring the user's health plan implementation status, the tracing unit can adjust the tracing method based on the user's current lifestyle rhythm. For example, if the user works the night shift, emphasis is placed on nighttime activity data, with daytime data used as a supplement. Also, if the user is traveling, priority is given to collecting data while traveling, and data that differs from the user's normal lifestyle rhythm can be taken into account. Furthermore, the tracing unit can adjust the frequency of tracing according to the user's lifestyle rhythm and collect data at appropriate times. This allows the tracing unit to adjust the tracing method based on the user's lifestyle rhythm, enabling more accurate health management.

[0053] When analyzing collected health information, the analysis unit can adjust the analysis algorithm based on the user's living environment. For example, if the user lives in an urban area, the analysis takes into account health risks specific to cities (air pollution, noise, etc.), and if the user lives in a rural area, the analysis takes into account health risks specific to rural areas (pesticides, animal contact, etc.). Furthermore, if the user lives in a nursing home, the analysis can take into account health risks specific to the elderly (falls, dementia, etc.). Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's living environment and provide appropriate analysis results. This allows the analysis unit to adjust the analysis algorithm based on the user's living environment, enabling more accurate analysis.

[0054] When monitoring the progress of a user's health plan, the tracing unit can customize the tracing method based on the user's past health data. For example, if the user has successfully completed an exercise plan in the past, tracing can be performed using a similar method, and if the user has failed a meal plan in the past, tracing can be performed using a different approach. Also, if the user has successfully completed a stress management plan in the past, tracing can be performed using that method as a reference. Furthermore, the tracing unit can adjust the frequency and level of detail of tracing based on past data to provide the user with an optimal tracing method. This allows the tracing unit to customize the tracing method based on past health data, enabling more effective health management.

[0055] When analyzing the collected health information, the analysis unit can determine the priority of the analysis based on the user's health goals. For example, if the user's goal is weight loss, the analysis unit prioritizes weight-related data (diet, exercise, etc.), and if the user's goal is muscle strength, the analysis unit prioritizes muscle strength-related data (training, nutrition, etc.). Furthermore, if the user's goal is stress management, the analysis unit can prioritize stress-related data (heart rate, sleep, etc.). Furthermore, the analysis unit can adjust the level of analysis detail according to the user's health goals and provide appropriate analysis results. This allows the analysis unit to prioritize analysis based on the user's health goals, enabling more effective health management.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects health information for each individual. The health information includes medical information, activity level information, dietary information, etc. The collection unit collects health information by acquiring data from wearable devices, manually entering the information, collecting sensor data, etc. Step 2: The analysis unit analyzes the health information collected by the collection unit. The analysis unit analyzes the health information using statistical analysis and machine learning algorithms to predict individual health risks. For example, health risks such as cardiovascular risk, diabetes risk, and obesity risk are predicted. Step 3: The recommendation unit recommends a treatment plan based on the analysis results obtained by the analysis unit. The recommendation unit can recommend treatment plans such as exercise plans, meal plans, and medical plans. Recommendations are made based on the user's past data or by algorithms. Step 4: The tracing unit traces the execution status of the plan recommended by the recommendation unit. The tracing unit monitors the execution status of the plan by acquiring data from the device or by the user's self-reporting, and modifies the plan as necessary.

[0058] (Example 2) A health management system according to an embodiment of the present invention links each individual's health information (e.g., medical information, activity level information), predicts potential health risks, recommends countermeasure plans, and tracks their implementation. This health management system collects each individual's health information and uses AI to analyze it. Based on the analysis results, the system then predicts individual health risks and recommends countermeasure plans. For example, it may recommend an appropriate exercise plan for someone who is not getting enough exercise, or a balanced meal plan for someone with an unbalanced diet. Furthermore, it tracks the implementation status of the recommended plan and modifies it as necessary. This allows the health management system to optimize each individual's health status and realize a healthy society.

[0059] A health management system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a tracing unit. The collection unit collects health information for each individual. The health information includes, but is not limited to, medical information, activity level information, and dietary information. The collection unit can collect health information by, for example, acquiring data from a wearable device, manually entering the information, or collecting sensor data. The analysis unit analyzes the health information collected by the collection unit. The analysis unit analyzes the health information using, for example, statistical analysis or a machine learning algorithm, and predicts individual health risks. The analysis unit can predict health risks such as cardiovascular risk, diabetes risk, and obesity risk. The recommendation unit recommends an action plan based on the analysis results obtained by the analysis unit. The recommendation unit can recommend action plans such as an exercise plan, a meal plan, and a medical plan. The recommendation unit can make recommendations based on the user's past data or an algorithm. The tracing unit traces the implementation status of the plan recommended by the recommendation unit. The tracing unit can monitor the execution status of the plan by, for example, acquiring data from devices or by users' self-reporting, and can modify the plan as necessary. This enables the health management system according to the embodiment to link the health information of each individual, predict health risks, recommend countermeasure plans, and trace execution.

[0060] The collection unit can collect at least one of health information, including medical information and activity amount information. The collection unit, for example, collects medical information. Medical information includes, but is not limited to, diagnostic results, prescriptions, and medical history. For example, the collection unit can acquire diagnostic results from an electronic medical record and prescription information from a pharmacy system. The collection unit can also collect activity amount information. Activity amount information includes, but is not limited to, the number of steps, exercise time, and calories burned. For example, the collection unit can acquire step count data from a wearable device and exercise time from a fitness app. In this way, the collection unit can collect medical information and activity amount information, thereby enabling the collection of comprehensive health information.

[0061] The analysis unit can analyze the collected health information and predict individual health risks. The analysis unit, for example, performs statistical analysis of the collected health information. Examples of statistical analysis include, but are not limited to, regression analysis, analysis of variance, and clustering. The analysis unit can, for example, use regression analysis to build a health risk prediction model and use analysis of variance to identify health risk factors. The analysis unit can also analyze the health information using machine learning algorithms. Examples of machine learning algorithms include, but are not limited to, decision trees, random forests, and neural networks. The analysis unit can, for example, use decision trees to build a health risk classification model and use random forests to improve the accuracy of health risk prediction. As a result, the analysis unit analyzes the health information and predicts individual health risks, enabling appropriate measures to be taken.

[0062] The recommendation unit can recommend a countermeasure plan based on the analysis results. The recommendation unit, for example, recommends an exercise plan based on the analysis results. Exercise plans include, for example, walking, jogging, and strength training, but are not limited to these examples. The recommendation unit can, for example, propose a walking plan and recommend a jogging plan. The recommendation unit can also recommend a meal plan based on the analysis results. Meal plans include, for example, a balanced diet, a low-calorie diet, and nutritional supplements, but are not limited to these examples. The recommendation unit can, for example, propose a balanced meal plan and recommend a low-calorie meal plan. As a result, when the recommendation unit recommends a countermeasure plan based on the analysis results, a plan tailored to each individual's health condition is provided.

[0063] The tracing unit can monitor the implementation status of the recommended plan and modify the plan as necessary. The tracing unit monitors the implementation status of the recommended plan, for example, by acquiring data from a device. Examples of devices include, but are not limited to, a wearable device, a smartphone, a fitness app, etc. The tracing unit can, for example, acquire data from a wearable device and collect data from a smartphone. The tracing unit can also monitor the implementation status of the plan through self-reports by the user. Examples of self-reports include, but are not limited to, diary-style reports, questionnaire-style reports, and reports through an app. The tracing unit can, for example, receive diary-style reports and collect questionnaire-style reports. This enables effective health management by the tracing unit monitoring the implementation status of the plan and modifying the plan as necessary.

[0064] The collection unit can estimate the user's emotions and adjust the timing of collecting health information based on the user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The collection unit can estimate the user's emotions using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The collection unit can also estimate the user's emotions using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. The collection unit can estimate the user's emotions using, for example, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. This allows the collection unit to adjust the timing of collecting health information according to the user's emotions, thereby enabling more appropriate information collection.

[0065] The collection unit can analyze the user's past health information collection history and select an appropriate collection method. For example, the collection unit acquires the user's past health information collection history from a database and analyzes it. The database includes, for example, past collection methods, collection frequencies, collection results, etc., but is not limited to these examples. For example, the collection unit can acquire past collection methods from the database and analyze collection frequencies. The collection unit can also select an optimal collection method from the past collection history. Collection methods include, for example, wearable devices, manual input, sensor data, etc., but are not limited to these examples. For example, the collection unit can select a collection method using a wearable device and recommend manual input. In this way, the collection unit can select an optimal collection method by analyzing the past collection history.

[0066] The collection unit may filter the health information based on the user's current living situation and areas of interest when collecting the health information. The collection unit may, for example, collect the user's current living situation through a questionnaire or interview. Examples of living situations include, but are not limited to, occupation, home environment, and daily activity patterns. For example, the collection unit may collect information about the user's occupation through a questionnaire and information about the home environment through an interview. The collection unit may also investigate the user's areas of interest and filter the health information when collecting the health information. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, and health goals. For example, the collection unit may collect information about hobbies through a questionnaire and information about health goals through an interview. In this way, the collection unit may filter the health information based on the user's living situation and areas of interest, thereby collecting highly relevant information.

[0067] The collection unit can estimate the user's emotions and determine the priority of health information to be collected based on the user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The collection unit can estimate the user's emotions using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The collection unit can also estimate the user's emotions using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. The collection unit can estimate the user's emotions using, for example, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. This allows the collection unit to determine the priority of health information based on the user's emotions, thereby allowing important information to be collected preferentially.

[0068] When collecting health information, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. The collection unit, for example, acquires the user's geographical location information using GPS data or address information. Examples of geographical location information include, but are not limited to, a current location, a movement history, and a specific region. For example, the collection unit can acquire the user's current location using GPS data and identify a specific region using address information. The collection unit can also prioritize collection of highly relevant health information based on the geographical location information. Examples of highly relevant information include, but are not limited to, region-specific health risks, local medical facility information, and local health event information. For example, the collection unit can prioritize collection of region-specific health risk information and acquire local medical facility information. In this way, the collection unit can collect region-specific health information by taking the geographical location information into consideration.

[0069] When collecting health information, the collection unit may analyze the user's social media activity and collect related information. For example, the collection unit may analyze the user's social media activity using data mining technology. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may analyze the content of posts and count the number of likes. The collection unit may also collect related health information based on the social media activity. For example, related information may include, but is not limited to, health topics of interest to the user, health information shared by followers, and health information currently trending on social media. For example, the collection unit may identify health topics of interest to the user and collect health information shared by followers. In this way, the collection unit may analyze the social media activity to collect health information of interest.

[0070] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the user's emotion. The analysis unit, for example, estimates the user's emotion using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The analysis unit can estimate the user's emotion using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The analysis unit can also estimate the user's emotion using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. The analysis unit can estimate the user's emotion using, for example, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. This allows the analysis unit to adjust the method of expressing the analysis according to the user's emotion, thereby providing more appropriate analysis results.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information. For example, the analysis unit evaluates the importance of the health information using an evaluation by a medical professional or statistical data. Examples of importance include, but are not limited to, the severity of the illness, the urgency of the symptoms, and the need for treatment. For example, the analysis unit can evaluate the severity of the illness based on an evaluation by a medical professional and the urgency of the symptoms using statistical data. The analysis unit can also adjust the level of detail of the analysis based on the importance. Examples of the level of detail include, but are not limited to, a detailed analysis, a simplified analysis, and an analysis that focuses on the main points. For example, the analysis unit can perform a detailed analysis of important health information and a simplified analysis of general health information. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information, thereby performing a detailed analysis of important information.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health information. For example, the analysis unit classifies the categories of health information based on the type of disease or the type of symptom. Examples of categories include, but are not limited to, cardiovascular disease, diabetes, and obesity. For example, the analysis unit can identify information related to cardiovascular disease and classify information related to diabetes. The analysis unit can also apply different analysis algorithms depending on the category. Examples of analysis algorithms include, but are not limited to, specialized analysis algorithms, exercise analysis algorithms, and nutrition analysis algorithms. For example, the analysis unit can apply a specialized analysis algorithm to cardiovascular disease and an exercise analysis algorithm to activity amount information. In this way, the analysis unit can apply analysis algorithms depending on the category of health information, thereby enabling more accurate analysis.

[0073] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the user's emotion. The analysis unit, for example, estimates the user's emotion using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The analysis unit can estimate the user's emotion using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The analysis unit can also estimate the user's emotion using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. The analysis unit can estimate the user's emotion using, for example, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. This allows the analysis unit to adjust the length of the analysis based on the user's emotion, thereby providing more appropriate analysis results.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the health information. The analysis unit, for example, obtains the collection time of the health information from a database and determines the priority of analysis. The collection time includes, for example, but is not limited to, recently collected information, information collected in the past, and information collected periodically. The analysis unit can, for example, prioritize analyzing recently collected information and refer to information collected in the past. The analysis unit can also adjust the priority of analysis based on the collection time. The priority includes, for example, prioritizing the most recent information, referring to past information, or prioritizing periodic information, but is not limited to, the above. The analysis unit can, for example, prioritize analyzing the most recent information and refer to past information. In this way, the analysis unit can prioritize analysis of the most recent information by determining the priority of analysis based on the collection time of the health information, allowing the most recent information to be analyzed preferentially.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health information. The analysis unit, for example, evaluates the relevance of the health information using co-occurrence analysis or correlation analysis. Relevance includes, but is not limited to, common factors, correlations, and causal relationships. For example, the analysis unit can identify common factors using co-occurrence analysis and evaluate correlations using correlation analysis. The analysis unit can also adjust the order of analysis based on the relevance. For example, the order can include, but is not limited to, prioritizing highly relevant information, postponing less relevant information, or grouping related information. For example, the analysis unit can prioritize analyzing highly relevant information and postponing less relevant information. As a result, by adjusting the order of analysis based on the relevance of the health information, highly relevant information can be prioritized for analysis.

[0076] The recommendation unit can estimate the user's emotions and adjust the way the recommendations are presented based on the user's emotions. The recommendation unit, for example, estimates the user's emotions using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The recommendation unit can estimate the user's emotions using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The recommendation unit can also estimate the user's emotions using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. The recommendation unit can estimate the user's emotions using, for example, an algorithm that analyzes voice tone and speed and a sensor that detects changes in voice. This allows the recommendation unit to adjust the way the recommendations are presented based on the user's emotions, thereby providing more appropriate recommendations.

[0077] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the health risk. The recommendation unit, for example, evaluates the importance of the health risk using an evaluation by a medical professional or statistical data. The importance includes, for example, the severity of the disease, the urgency of the symptoms, and the need for treatment, but is not limited to these examples. The recommendation unit can, for example, evaluate the severity of the disease based on the evaluation by a medical professional and evaluate the urgency of the symptoms using statistical data. The recommendation unit can also adjust the level of detail of the recommendation based on the importance. The level of detail includes, for example, a detailed recommendation, a simplified recommendation, a recommendation that focuses on the main points, but is not limited to these examples. The recommendation unit can, for example, provide a detailed recommendation for an important health risk and a simplified recommendation for a general health risk. In this way, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the health risk, thereby making a detailed recommendation for an important risk.

[0078] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the health risk category. For example, the recommendation unit classifies health risk categories based on the type of disease or the type of symptom. Examples of categories include, but are not limited to, cardiovascular disease, diabetes, and obesity. For example, the recommendation unit can identify information related to cardiovascular disease and classify information related to diabetes. The recommendation unit can also apply different recommendation algorithms depending on the category. Examples of recommendation algorithms include, but are not limited to, an algorithm for recommending an exercise plan, an algorithm for recommending a meal plan, and an algorithm for recommending a relaxation plan. For example, the recommendation unit can apply an algorithm for recommending an exercise plan for lack of exercise, and an algorithm for recommending a meal plan for unbalanced diet. In this way, the recommendation unit can apply a recommendation algorithm according to the health risk category, thereby making more accurate recommendations.

[0079] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of occurrence of the health risk. The recommendation unit, for example, obtains the time of occurrence of the health risk from a database and determines the priority of the recommendation. The time of occurrence includes, for example, but is not limited to, the most recent occurrence, past occurrence, and periodic occurrence. The recommendation unit can, for example, prioritize the most recent occurrence and refer to past occurrences. The recommendation unit can also adjust the priority of the recommendation based on the time of occurrence. The priority includes, for example, prioritizing the most recent risk, referring to past risks, and emphasizing periodic risks, but is not limited to, the example. The recommendation unit can, for example, prioritize the most recent risk and refer to past risks. In this way, the recommendation unit can determine the priority of the recommendation based on the time of occurrence of the health risk, thereby enabling a prompt response to the most recent risk.

[0080] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of health risks. The recommendation unit, for example, evaluates the relevance of health risks using co-occurrence analysis or correlation analysis. Relevance includes, for example, common factors, correlations, and causal relationships, but is not limited to these examples. The recommendation unit can, for example, identify common factors using co-occurrence analysis and evaluate correlations using correlation analysis. The recommendation unit can also adjust the order of recommendations based on the relevance. The order includes, for example, prioritizing highly relevant risks, postponing less relevant risks, and grouping related risks, but is not limited to these examples. The recommendation unit can, for example, prioritize highly relevant risks and postpone less relevant risks. In this way, by the recommendation unit adjusting the order of recommendations based on the relevance of health risks, highly relevant risks can be addressed preferentially.

[0081] The tracing unit can estimate the user's emotions and adjust the tracing method based on the user's emotions. The tracing unit, for example, estimates the user's emotions using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The tracing unit can estimate the user's emotions using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The tracing unit can also estimate the user's emotions using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. The tracing unit can estimate the user's emotions using, for example, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. This allows the tracing unit to adjust the tracing method according to the user's emotions, thereby enabling more appropriate tracing.

[0082] During tracing, the tracing unit can analyze the user's past plan execution history and select the optimal tracing method. The tracing unit, for example, acquires the user's past plan execution history from a database and analyzes it. The execution history includes, for example, past plan execution status, success rate, execution frequency, etc., but is not limited to these examples. The tracing unit can, for example, acquire the past plan execution status from the database and analyze the success rate. The tracing unit can also select the optimal tracing method from the past execution history. The tracing method includes, for example, a simple tracing method, a detailed tracing method, a tracing method that focuses on the main points, etc., but is not limited to these examples. The tracing unit can, for example, preferentially suggest plan execution methods that have been successful in the past and optimize the tracing frequency based on the past execution history. In this way, the tracing unit can select the optimal tracing method by analyzing the past plan execution history.

[0083] The tracing unit can customize the tracing means based on the user's current living situation during tracing. The tracing unit, for example, collects the user's current living situation through a questionnaire or interview. The living situation includes, but is not limited to, for example, occupation, home environment, and daily activity patterns. For example, the tracing unit can collect information about the user's occupation through a questionnaire and information about the home environment through an interview. The tracing unit can also customize the tracing means based on the living situation. For example, the tracing means can include, but are not limited to, a simple tracing means, a detailed tracing means, and a tracing means that focuses on the main points. For example, the tracing unit can prioritize relevant tracing means based on the current living situation and flexibly change the tracing means depending on the living situation. This allows the tracing unit to customize the tracing means based on the user's living situation, thereby enabling more effective tracing.

[0084] The tracing unit can estimate the user's emotions and determine the priority of tracing based on the user's emotions. The tracing unit, for example, estimates the user's emotions using facial expression recognition technology. Examples of facial expression recognition technology include, but are not limited to, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The tracing unit can estimate the user's emotions using, for example, an algorithm that analyzes facial expressions and a sensor that detects changes in facial expressions. The tracing unit can also estimate the user's emotions using voice analysis technology. Examples of voice analysis technology include, but are not limited to, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. The tracing unit can estimate the user's emotions using, for example, an algorithm that analyzes the tone and speed of voice and a sensor that detects changes in voice. This allows the tracing unit to determine the priority of tracing based on the user's emotions, thereby prioritizing important tracing.

[0085] During tracing, the tracing unit can select an appropriate tracing method based on the user's geographical location information. The tracing unit, for example, acquires the user's geographical location information using GPS data or address information. Examples of geographical location information include, but are not limited to, a current location, a movement history, and a specific region. For example, the tracing unit can acquire the user's current location using GPS data and identify a specific region using address information. The tracing unit can also select an appropriate tracing method based on the geographical location information. Examples of tracing methods include, but are not limited to, a region-specific tracing method, a general tracing method, a customized tracing method, and the like. For example, the tracing unit can preferentially provide tracing methods related to a specific region and select a region-specific tracing method. This allows the tracing unit to select a region-specific tracing method by taking the geographical location information into consideration.

[0086] During tracing, the tracing unit can analyze the user's social media activity and suggest tracing methods. The tracing unit, for example, analyzes the user's social media activity using data mining technology. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The tracing unit can, for example, analyze the content of posts and count the number of likes. The tracing unit can also suggest tracing methods based on the social media activity. Tracing methods include, for example, but are not limited to, simple tracing methods, detailed tracing methods, and key tracing methods. The tracing unit can, for example, suggest tracing methods of interest from the social media activity and provide related tracing methods preferentially. In this way, the tracing unit can suggest tracing methods of interest by analyzing the social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and tracing 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 collection unit collects health information through sensor data of the smart device 14 or manual input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected health information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a response plan based on the analysis results. The tracing unit is realized, for example, by the control unit 46A of the smart device 14 and traces the execution status of the recommended plan. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and tracing 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 collection unit collects health information through sensor data of the smart glasses 214 or manual input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected health information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a response plan based on the analysis results. The tracing unit is realized, for example, by the control unit 46A of the smart glasses 214 and traces the execution status of the recommended plan. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and tracing 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 collection unit collects health information through sensor data of the headset type terminal 314 or manual input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected health information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a response plan based on the analysis results. The tracing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and traces the execution status of the recommended plan. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and tracing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health information through sensor data of the robot 414 or manual input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected health information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a response plan based on the analysis results. The tracing unit is realized, for example, by the control unit 46A of the robot 414 and traces the execution status of the recommended plan.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When collecting the user's health information, the collection unit can measure the user's current stress level and adjust the collection method based on the result. For example, if the user is in a high-stress state, the collection unit can automatically select a data collection method to reduce the user's burden. Also, if the user is relaxed, the collection unit can request a detailed self-report. Furthermore, the collection unit can adjust the collection frequency according to the stress level to more accurately grasp the user's health information. As a result, by the collection unit adjusting the collection method based on the user's stress level, it is possible to collect accurate health information while reducing the user's burden.

[0089] When analyzing collected health information, the analysis unit can compare it with the user's past health data to detect abnormal values. For example, it can compare the user's past blood pressure data with current data and issue a warning if there are abnormal fluctuations. It can also compare past exercise data with current data and warn the user if there is a sudden change in the amount of exercise. Furthermore, the analysis unit can compare past dietary data with current data and suggest improvements if there is an imbalance in nutritional balance. In this way, by detecting abnormal values ​​by comparing with past data, the analysis unit can discover health risks early and take appropriate measures.

[0090] The recommendation unit can estimate the user's emotions and customize the recommendation content based on the user's emotions. For example, if the user is feeling stressed, a relaxation plan can be recommended with priority, and if the user is relaxed, an exercise plan or a meal plan can be suggested. Also, if the user is excited, a meditation plan to calm down can be provided. Furthermore, the recommendation unit can adjust the way recommendations are presented according to the user's emotions and provide information in a form that is easy for the user to accept. This allows the recommendation unit to customize the recommendation content based on the user's emotions, enabling more effective health management.

[0091] When monitoring the user's health plan implementation status, the tracing unit can adjust the tracing method based on the user's current lifestyle rhythm. For example, if the user works the night shift, emphasis is placed on nighttime activity data, with daytime data used as a supplement. Also, if the user is traveling, priority is given to collecting data while traveling, and data that differs from the user's normal lifestyle rhythm can be taken into account. Furthermore, the tracing unit can adjust the frequency of tracing according to the user's lifestyle rhythm and collect data at appropriate times. This allows the tracing unit to adjust the tracing method based on the user's lifestyle rhythm, enabling more accurate health management.

[0092] The collection unit can estimate the user's emotions and select the type of health information to collect based on the user's emotions. For example, if the user is feeling stressed, stress-related health information (heart rate, blood pressure, etc.) is collected preferentially, and if the user is relaxed, general health information (number of steps, calorie consumption, etc.) is collected. Furthermore, if the user is excited, data related to emotional fluctuations (facial expressions, voice tone, etc.) can be collected. Furthermore, the collection unit can adjust the collection method according to the user's emotions to reduce the burden on the user. This allows the collection unit to select the type of health information to collect based on the user's emotions, enabling more appropriate information collection.

[0093] When analyzing collected health information, the analysis unit can adjust the analysis algorithm based on the user's living environment. For example, if the user lives in an urban area, the analysis takes into account health risks specific to cities (air pollution, noise, etc.), and if the user lives in a rural area, the analysis takes into account health risks specific to rural areas (pesticides, animal contact, etc.). Furthermore, if the user lives in a nursing home, the analysis can take into account health risks specific to the elderly (falls, dementia, etc.). Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's living environment and provide appropriate analysis results. This allows the analysis unit to adjust the analysis algorithm based on the user's living environment, enabling more accurate analysis.

[0094] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the user's emotions. For example, if the user is feeling stressed, it can immediately recommend a relaxation plan, and if the user is relaxed, it can suggest the next health plan. Also, if the user is excited, it can provide a meditation plan to calm the user. Furthermore, the recommendation unit can adjust the frequency of recommendations according to the user's emotions and provide information at a timing that is easy for the user to accept. This allows the recommendation unit to adjust the timing of recommendations based on the user's emotions, enabling more effective health management.

[0095] When monitoring the progress of a user's health plan, the tracing unit can customize the tracing method based on the user's past health data. For example, if the user has successfully completed an exercise plan in the past, tracing can be performed using a similar method, and if the user has failed a meal plan in the past, tracing can be performed using a different approach. Also, if the user has successfully completed a stress management plan in the past, tracing can be performed using that method as a reference. Furthermore, the tracing unit can adjust the frequency and level of detail of tracing based on past data to provide the user with an optimal tracing method. This allows the tracing unit to customize the tracing method based on past health data, enabling more effective health management.

[0096] The collection unit can estimate the user's emotions and adjust the accuracy of the data to be collected based on the user's emotions. For example, if the user is feeling stressed, simplified data collection is performed to reduce the burden on the user. If the user is relaxed, detailed data collection is performed to collect more accurate information. Furthermore, if the user is excited, data related to emotional fluctuations can be preferentially collected. As a result, the collection unit can adjust the accuracy of the data based on the user's emotions, thereby reducing the burden on the user and enabling appropriate information collection.

[0097] When analyzing the collected health information, the analysis unit can determine the priority of the analysis based on the user's health goals. For example, if the user's goal is weight loss, the analysis unit prioritizes weight-related data (diet, exercise, etc.), and if the user's goal is muscle strength, the analysis unit prioritizes muscle strength-related data (training, nutrition, etc.). Furthermore, if the user's goal is stress management, the analysis unit can prioritize stress-related data (heart rate, sleep, etc.). Furthermore, the analysis unit can adjust the level of analysis detail according to the user's health goals and provide appropriate analysis results. This allows the analysis unit to prioritize analysis based on the user's health goals, enabling more effective health management.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection unit collects health information for each individual. The health information includes medical information, activity level information, dietary information, etc. The collection unit collects health information by acquiring data from wearable devices, manually entering the information, collecting sensor data, etc. Step 2: The analysis unit analyzes the health information collected by the collection unit. The analysis unit analyzes the health information using statistical analysis and machine learning algorithms to predict individual health risks. For example, health risks such as cardiovascular risk, diabetes risk, and obesity risk are predicted. Step 3: The recommendation unit recommends a treatment plan based on the analysis results obtained by the analysis unit. The recommendation unit can recommend treatment plans such as exercise plans, meal plans, and medical plans. Recommendations are made based on the user's past data or by algorithms. Step 4: The tracing unit traces the execution status of the plan recommended by the recommendation unit. The tracing unit monitors the execution status of the plan by acquiring data from the device or by the user's self-reporting, and modifies the plan as necessary.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects health information; an analysis unit that analyzes the information collected by the collection unit; a recommendation unit that recommends a response plan based on the analysis result obtained by the analysis unit; a tracing unit that traces the execution status of the plan recommended by the recommendation unit; Equipped with A system characterized by:

2. The collecting unit Collect at least one of medical information and activity information. The system of claim 1 .

3. The analysis unit Analyzing collected health information and predicting individual health risks The system of claim 1 .

4. The recommendation unit Recommend a response plan based on the analysis results The system of claim 1 .

5. The trace portion is Monitor the execution of the recommended plan and modify it as needed The system of claim 1 .

6. The collecting unit Estimates user emotions and adjusts the timing of health information collection based on the user emotions The system of claim 1 .

7. The collecting unit Analyze the user's past health information collection history and select the appropriate collection method The system of claim 1 .

8. The collecting unit When collecting health information, filter it based on the user's current lifestyle and areas of interest. The system of claim 1 .

Images