system

Abstract

The system according to the embodiment aims to comprehensively assess the health risks of a user and link them to appropriate medical services. [Solution] The system according to the embodiment includes a reception unit, a determination unit, a provision unit, and a linking unit. The reception unit receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from a user. The determination unit analyzes the information received by the reception unit and determines current and future health risks. The provision unit provides the user with the health risks determined by the determination unit. The linking unit links with online health consultation and medical services based on the information provided by the provision 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 have had the problem of not being able to comprehensively assess a user's health risks and link them to appropriate medical services.

[0005] The system according to the embodiment aims to comprehensively assess the health risks of a user and link them to appropriate medical services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a determination unit, a provision unit, and a linking unit. The reception unit receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from a user. The determination unit analyzes the information received by the reception unit and determines current and future health risks. The provision unit provides the user with the health risks determined by the determination unit. The linking unit links with online health consultation and medical services based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively assess the health risks of a user and link with appropriate medical services. [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 risk assessment system according to an embodiment of the present invention allows a user to input and share health-related data, and uses a generating AI to assess current and future health risks and provide the results to the user. In the health risk assessment system, users input and share medical data, medical history, biometric information, lifestyle habits, and dietary data. The generating AI analyzes this data and assesses current and future health risks. The assessment results are provided to the user, and in collaboration with online health consultation and medical services, the system utilizes the user's information for consultation and diagnosis. For example, in a health risk assessment system, a user inputs the results of a health checkup, links heart rate and step count data measured by a smartwatch, and inputs dietary details and exercise habits. This information is input into a generating AI, which then analyzes the input information. The generating AI analyzes the medical data, medical history, biometric information, lifestyle habits, and dietary data to assess current and future health risks. For example, the generating AI analyzes the results of a health checkup and determines a high risk of heart disease. The system also analyzes smartwatch data to assess future health risks due to lack of exercise and analyzes dietary habits to identify nutritional imbalances. The AI ​​then notifies the user of their risk of heart disease, health risks due to lack of exercise, and nutritional imbalances. Users can use this information to manage their health. Furthermore, the health risk assessment system can link with online health consultation and medical services to utilize the user's information for consultations and diagnoses. For example, when a user uses an online health consultation service, the AI ​​provides the analyzed health risk information, which doctors use to provide diagnoses and advice. This allows users to receive more appropriate health management and treatment. This allows the health risk assessment system to grasp the user's health status and predict future health risks. Linking with online health consultation and medical services also allows users to receive more appropriate health management and treatment. For example, if a user is determined to be at high risk for heart disease, a doctor can use this information to suggest appropriate treatments. This improves the user's health and reduces future health risks.

[0029] A health risk assessment system according to an embodiment includes a reception unit, a determination unit, a provision unit, and a linking unit. The reception unit receives input of medical data, medical history, biofunction measurement information, and lifestyle or dietary data from a user. The medical data from the user includes, for example, health checkup results, comprehensive medical examination results, and medical institution test results. The medical history includes past medical history, surgical history, and allergy information. The biofunction measurement information includes, for example, heart rate, blood pressure, and body temperature. The lifestyle includes exercise habits, smoking habits, and drinking habits. The dietary data includes dietary content, calorie intake, and nutrient information. For example, the reception unit receives the results of a health checkup from a user, links heart rate and step count data measured by a smartwatch, and accepts the input of dietary content and exercise habits. The determination unit uses a generation AI to analyze the information received by the reception unit and determine current and future health risks. For example, the generation AI analyzes the results of a health checkup and determines a high risk of heart disease. The generating AI can also analyze smartwatch data to determine future health risks due to lack of exercise and analyze dietary content to identify nutritional imbalances. For example, the generating AI can receive health checkup results as input and output the risk of heart disease. The generating AI can also receive smartwatch data as input and output health risks due to lack of exercise. The generating AI can also receive dietary content as input and output nutritional imbalances. The providing unit provides the user with the health risks determined by the determining unit. The providing unit notifies the user of, for example, the risk of heart disease, health risks due to lack of exercise, and nutritional imbalances determined by the generating AI. The user can manage their health based on this information. For example, the providing unit displays the health risks determined by the generating AI to the user through a web application or mobile application. The linking unit links with online health consultation and medical services based on the information provided by the providing unit. For example, when a user uses an online health consultation, the linking unit provides the user with health risk information analyzed by the generating AI, and doctors use this information to make diagnoses and provide advice.This allows users to receive more appropriate health management and treatment. For example, the linking unit provides health risk information determined by the generation AI to an online health consultation service, and doctors use this information to make diagnoses and provide advice. This allows the health risk determination system according to the embodiment to grasp the user's health status and predict future health risks. Furthermore, by linking with online health consultation and medical services, users can receive more appropriate health management and treatment.

[0030] The reception unit can analyze the user's past data input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also select an optimal input method based on the type of data the user has previously input. In this way, by analyzing the past data input history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history to a generation AI and have the generation AI select an optimal input method.

[0031] The reception unit can filter data based on the user's current health condition and areas of interest when inputting data. The reception unit filters data items that require input based on the user's current health condition, for example. The reception unit can also preferentially display data items that require input based on the user's areas of interest. The reception unit can also hide data items that do not require input based on the user's health condition and areas of interest. This enables efficient data input by filtering data input based on the user's health condition and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition data to a generation AI and have the generation AI perform filtering.

[0032] The reception unit can select an appropriate input means depending on the user's input method when inputting data. For example, if the user desires voice input, the reception unit inputs data using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also provide keyboard input. Furthermore, if the user desires image input, the reception unit can also input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal input means.

[0033] When inputting data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize inputting data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting data related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting data related to the home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0034] The reception unit can analyze the user's social media activity and input relevant data when inputting data. For example, the reception unit automatically inputs health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input relevant data. The reception unit can also input relevant data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, relevant data can be input efficiently. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input relevant data.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit customizes the input method, for example, based on feedback provided by the user in the past. The reception unit can also select the optimal input means from the user's past feedback. The reception unit can also improve the input interface by referring to the user's past feedback. In this way, the optimal input method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] The determination unit can adjust the level of detail of the determination based on the importance of the data when determining a health risk. The determination unit, for example, performs a detailed health risk determination based on data with high importance. The determination unit can also perform a simplified health risk determination based on data with low importance. The determination unit can also dynamically adjust the level of detail of the determination based on the importance of the data. This enables efficient health risk determination by adjusting the level of detail of the determination based on the importance of the data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the determination.

[0037] The determination unit can apply different determination algorithms depending on the data category when determining health risks. For example, the determination unit applies a medical-specific determination algorithm to medical data. The determination unit can also apply a biological function-specific determination algorithm to biological function measurement information. The determination unit can also apply a lifestyle-specific determination algorithm to lifestyle and dietary data. This enables highly accurate health risk determination by applying different determination algorithms depending on the data category. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the data category to the generation AI and cause the generation AI to apply different determination algorithms.

[0038] The determination unit can improve the accuracy of the health risk determination by referring to the user's past determination results. For example, the determination unit can improve the current determination accuracy by referring to the user's past health risk determination results. The determination unit can also focus on determining specific health risks based on the user's past determination results. The determination unit can also analyze the user's past determination results and optimize the determination algorithm. This can improve the current determination accuracy by referring to the past determination results. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's past determination result data into the generation AI and cause the generation AI to improve the determination accuracy.

[0039] When assessing health risks, the assessment unit can determine the priority of assessment based on the time of data submission. For example, the assessment unit prioritizes the most recently submitted data. The assessment unit can also lower the priority of older submitted data. The assessment unit can also dynamically adjust the priority of assessment based on the time of submission. This enables efficient health risk assessment by determining the priority of assessment based on the time of data submission. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input the time of data submission to the generation AI and have the generation AI determine the priority of assessment.

[0040] The determination unit can adjust the order of determination based on the relevance of data when determining health risks. For example, the determination unit prioritizes determination of highly relevant data. The determination unit can also postpone the order of determination of less relevant data. The determination unit can also dynamically adjust the order of determination based on the relevance of data. This enables efficient health risk determination by adjusting the order of determination based on the relevance of data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of determination.

[0041] When determining a health risk, the determination unit can adjust the use of technical terms in the determination result according to the user's level of expertise. For example, the determination unit can explain the determination result in simple terms to a user with little technical knowledge. The determination unit can also explain the determination result using detailed technical terms to a user with extensive technical knowledge. The determination unit can also dynamically adjust the method of explaining the determination result according to the user's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terms in the determination result according to the user's level of expertise. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0042] When notifying a health risk, the providing unit can adjust the level of detail of the notification based on the importance of the risk. For example, the providing unit provides a detailed notification for a risk with a high importance. The providing unit can also provide a simplified notification for a risk with a low importance. The providing unit can also dynamically adjust the level of detail of the notification according to the importance of the risk. This enables efficient information provision by adjusting the level of detail of the notification according to the importance of the risk. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the risk to the generating AI and cause the generating AI to adjust the level of detail of the notification.

[0043] When notifying a health risk, the providing unit can apply different notification algorithms depending on the risk category. For example, the providing unit can apply a notification algorithm specialized for heart disease to heart disease risk. The providing unit can also apply a notification algorithm specialized for diabetes to diabetes risk. The providing unit can also apply a notification algorithm specialized for cancer to cancer risk. This makes it possible to provide highly accurate information by applying different notification algorithms depending on the risk category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the risk category into the generating AI and cause the generating AI to apply different notification algorithms.

[0044] When notifying a health risk, the providing unit can improve the accuracy of the notification by referring to the user's past notification results. For example, the providing unit can improve the accuracy of the current notification by referring to the user's past health risk notification results. The providing unit can also focus the notification on specific risks based on the user's past notification results. The providing unit can also analyze the user's past notification results and optimize the notification algorithm. In this way, the accuracy of the current notification can be improved by referring to the past notification results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past notification result data into the generation AI and cause the generation AI to improve the accuracy of the notification.

[0045] When notifying a health risk, the providing unit can determine the priority of notifications based on the time of submission of the risk. For example, the providing unit prioritizes notification of recently submitted risk information. The providing unit can also lower the priority of risk information that was submitted earlier. The providing unit can also dynamically adjust the priority of notifications based on the time of submission. This enables efficient information provision by determining the priority of notifications based on the time of submission of the risk. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the risk to the generating AI and have the generating AI determine the priority of notifications.

[0046] The providing unit can adjust the order of notifications based on the relevance of the risks when notifying health risks. For example, the providing unit prioritizes the notification of highly relevant risk information. The providing unit can also postpone the order of notifications for less relevant risk information. The providing unit can also dynamically adjust the order of notifications based on the relevance of the risk information. This enables efficient information provision by adjusting the order of notifications based on the relevance of the risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the risk information to the generating AI and cause the generating AI to adjust the order of notifications.

[0047] When notifying a user of a health risk, the providing unit can adjust the use of technical terminology in the notification according to the user's level of expertise. For example, the providing unit can provide a notification in simple language to a user with little technical expertise. The providing unit can also provide a notification using detailed technical terminology to a user with extensive technical expertise. The providing unit can also dynamically adjust the content of the notification according to the user's level of expertise. This enables more appropriate information to be provided by adjusting the use of technical terminology in the notification according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into a generating AI and cause the generating AI to use technical terminology.

[0048] When linking services, the linking unit can select the optimal linking method based on the user's past linking history. For example, the linking unit prioritizes linking services that the user has used in the past. The linking unit can also suggest the optimal linking method based on the user's past linking history. The linking unit can also analyze the user's past linking history and optimize the linking method. This makes it possible to provide the optimal linking method by referring to the past linking history. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's past linking history data into the generation AI and cause the generation AI to select the optimal linking method.

[0049] The collaboration unit can customize the collaboration content based on the user's current health condition during service collaboration. The collaboration unit customizes the content of the service to be collaborated based on, for example, the user's current health condition. The collaboration unit can also adjust the priority of the services to be collaborated according to the user's health condition. The collaboration unit can also customize the method of providing the services to be collaborated based on the user's health condition. This enables more appropriate service provision by customizing the collaboration content based on the user's current health condition. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's health condition data into the generation AI and cause the generation AI to customize the collaboration content.

[0050] The collaboration unit can improve the collaboration method by reflecting user feedback during service collaboration. The collaboration unit improves the collaboration method based on, for example, user feedback. The collaboration unit can also propose an optimal collaboration method based on the user's past feedback. The collaboration unit can also improve the collaboration interface by referring to the user's feedback. In this way, the collaboration method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0051] The linking unit can select the optimal linking method when linking services, taking into account the user's geographical location information. For example, when the user is in a specific area, the linking unit prioritizes linking services related to that area. Furthermore, when the user is traveling, the linking unit can also prioritize linking services related to the travel destination. Furthermore, when the user is at home, the linking unit can also prioritize linking services related to the home. In this way, the optimal linking method can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal linking method.

[0052] When linking services, the linking unit can analyze the user's social media activity and suggest linking content. For example, the linking unit can suggest related services based on health information shared by the user on social media. The linking unit can also analyze the content posted by the user on social media and suggest related services. The linking unit can also suggest related services by referring to the activities of the user's friends on social media. In this way, related services can be efficiently suggested by analyzing social media activity. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into the generation AI and have the generation AI suggest linking content.

[0053] The collaboration unit can customize the collaboration method by reflecting the user's past feedback when collaborating with services. The collaboration unit customizes the collaboration method based on, for example, the user's past feedback. The collaboration unit can also propose an optimal collaboration method based on the user's past feedback. The collaboration unit can also improve the collaboration interface by referring to the user's past feedback. In this way, the collaboration method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collaboration method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When the user enters health data, the reception unit can analyze the user's past data input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user tends to input data during a specific time period, the reception unit can send a notification prompting input during that time period. Furthermore, the reception unit can select the optimal input method based on the type of data the user has input in the past. In this way, by analyzing the user's past data input history, the reception unit can offer the optimal input method to the user.

[0056] The reception unit can perform filtering based on the user's current health condition and areas of interest when inputting data. For example, data items that require input can be filtered based on the user's current health condition. Data items that require input can also be preferentially displayed based on the user's areas of interest. Furthermore, data items that do not require input can also be hidden based on the user's health condition and areas of interest. This allows for efficient data input by filtering data input based on the user's health condition and areas of interest.

[0057] When inputting data, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the reception unit can input data using voice recognition technology. If the user desires text input, the reception unit can also provide keyboard input. Furthermore, if the user desires image input, the reception unit can also input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method.

[0058] The determination unit can adjust the level of detail of the determination based on the importance of the data when determining a health risk. For example, a detailed health risk determination is made based on data with high importance. A simplified health risk determination can also be made based on data with low importance. Furthermore, the level of detail of the determination can be dynamically adjusted according to the importance of the data. This allows for efficient health risk determination by adjusting the level of detail of the determination according to the importance of the data.

[0059] When determining health risks, the determination unit can apply different determination algorithms depending on the data category. For example, a medical-specific determination algorithm can be applied to medical data. A biological function-specific determination algorithm can also be applied to biological function measurement information. Furthermore, a lifestyle-specific determination algorithm can also be applied to lifestyle and dietary data. This allows for highly accurate health risk determination by applying different determination algorithms depending on the data category.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts input of medical data, medical history, biometric information, lifestyle or dietary data from the user. For example, this may include the results of a health checkup, medical examination, medical examination results, past medical history, surgical history, allergy information, heart rate, blood pressure, body temperature, exercise habits, smoking habits, drinking habits, dietary content, calorie intake, and nutrient information. Step 2: The judgment unit uses the generation AI to analyze the information received by the reception unit and determine current and future health risks. For example, it analyzes health checkup results to determine the risk of heart disease, analyzes smartwatch data to determine future health risks due to lack of exercise, and analyzes dietary content to point out nutritional imbalances. Step 3: The provider provides the user with the health risks determined by the determiner. For example, the provider displays the health risks determined by the generator AI, such as the risk of heart disease, health risks due to lack of exercise, and nutritional imbalance, via a web or mobile application to notify the user. Step 4: The linking unit links with online health consultation and medical services based on the information provided by the provider. For example, when a user uses online health consultation, the AI ​​generation unit provides the analyzed health risk information, and a doctor uses this information to make a diagnosis and provide advice.

[0062] (Example 2) A health risk assessment system according to an embodiment of the present invention allows a user to input and share health-related data, and uses a generating AI to assess current and future health risks and provide the results to the user. In the health risk assessment system, users input and share medical data, medical history, biometric information, lifestyle habits, and dietary data. The generating AI analyzes this data and assesses current and future health risks. The assessment results are provided to the user, and in collaboration with online health consultation and medical services, the system utilizes the user's information for consultation and diagnosis. For example, in a health risk assessment system, a user inputs the results of a health checkup, links heart rate and step count data measured by a smartwatch, and inputs dietary details and exercise habits. This information is input into a generating AI, which then analyzes the input information. The generating AI analyzes the medical data, medical history, biometric information, lifestyle habits, and dietary data to assess current and future health risks. For example, the generating AI analyzes the results of a health checkup and determines a high risk of heart disease. The system also analyzes smartwatch data to assess future health risks due to lack of exercise and analyzes dietary habits to identify nutritional imbalances. The AI ​​then notifies the user of their risk of heart disease, health risks due to lack of exercise, and nutritional imbalances. Users can use this information to manage their health. Furthermore, the health risk assessment system can link with online health consultation and medical services to utilize the user's information for consultations and diagnoses. For example, when a user uses an online health consultation service, the AI ​​provides the analyzed health risk information, which doctors use to provide diagnoses and advice. This allows users to receive more appropriate health management and treatment. This allows the health risk assessment system to grasp the user's health status and predict future health risks. Linking with online health consultation and medical services also allows users to receive more appropriate health management and treatment. For example, if a user is determined to be at high risk for heart disease, a doctor can use this information to suggest appropriate treatments. This improves the user's health and reduces future health risks.

[0063] A health risk assessment system according to an embodiment includes a reception unit, a determination unit, a provision unit, and a linking unit. The reception unit receives input of medical data, medical history, biofunction measurement information, and lifestyle or dietary data from a user. The medical data from the user includes, for example, health checkup results, comprehensive medical examination results, and medical institution test results. The medical history includes past medical history, surgical history, and allergy information. The biofunction measurement information includes, for example, heart rate, blood pressure, and body temperature. The lifestyle includes exercise habits, smoking habits, and drinking habits. The dietary data includes dietary content, calorie intake, and nutrient information. For example, the reception unit receives the results of a health checkup from a user, links heart rate and step count data measured by a smartwatch, and accepts the input of dietary content and exercise habits. The determination unit uses a generation AI to analyze the information received by the reception unit and determine current and future health risks. For example, the generation AI analyzes the results of a health checkup and determines a high risk of heart disease. The generating AI can also analyze smartwatch data to determine future health risks due to lack of exercise and analyze dietary content to identify nutritional imbalances. For example, the generating AI can receive health checkup results as input and output the risk of heart disease. The generating AI can also receive smartwatch data as input and output health risks due to lack of exercise. The generating AI can also receive dietary content as input and output nutritional imbalances. The providing unit provides the user with the health risks determined by the determining unit. The providing unit notifies the user of, for example, the risk of heart disease, health risks due to lack of exercise, and nutritional imbalances determined by the generating AI. The user can manage their health based on this information. For example, the providing unit displays the health risks determined by the generating AI to the user through a web application or mobile application. The linking unit links with online health consultation and medical services based on the information provided by the providing unit. For example, when a user uses an online health consultation, the linking unit provides the user with health risk information analyzed by the generating AI, and doctors use this information to make diagnoses and provide advice.This allows users to receive more appropriate health management and treatment. For example, the linking unit provides health risk information determined by the generation AI to an online health consultation service, and doctors use this information to make diagnoses and provide advice. This allows the health risk determination system according to the embodiment to grasp the user's health status and predict future health risks. Furthermore, by linking with online health consultation and medical services, users can receive more appropriate health management and treatment.

[0064] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of data input and encourage the user to input in a relaxed state. Furthermore, if the user is relaxed, the reception unit can also speed up the timing of data input to allow the user to input efficiently. Furthermore, if the user is in a hurry, the reception unit can also adjust the timing of data input to complete input in a short time. This allows for more appropriate data input by adjusting the timing of data input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past data input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also select an optimal input method based on the type of data the user has previously input. In this way, by analyzing the past data input history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history to a generation AI and have the generation AI select an optimal input method.

[0066] The reception unit can filter data based on the user's current health condition and areas of interest when inputting data. The reception unit filters data items that require input based on the user's current health condition, for example. The reception unit can also preferentially display data items that require input based on the user's areas of interest. The reception unit can also hide data items that do not require input based on the user's health condition and areas of interest. This enables efficient data input by filtering data input based on the user's health condition and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition data to a generation AI and have the generation AI perform filtering.

[0067] The reception unit can select an appropriate input means depending on the user's input method when inputting data. For example, if the user desires voice input, the reception unit inputs data using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also provide keyboard input. Furthermore, if the user desires image input, the reception unit can also input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal input means.

[0068] The reception unit can estimate the user's emotions and determine the priority of input data based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes input of important data items. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed data items. Furthermore, when the user is in a hurry, the reception unit can prioritize input of a minimum number of data items. Thus, by determining the priority of input data according to the user's emotions, important data can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When inputting data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize inputting data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting data related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting data related to the home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0070] The reception unit can analyze the user's social media activity and input relevant data when inputting data. For example, the reception unit automatically inputs health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input relevant data. The reception unit can also input relevant data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, relevant data can be input efficiently. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input relevant data.

[0071] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit customizes the input method, for example, based on feedback provided by the user in the past. The reception unit can also select the optimal input means from the user's past feedback. The reception unit can also improve the input interface by referring to the user's past feedback. In this way, the optimal input method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0072] The determination unit can estimate the user's emotions and adjust the health risk assessment method based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit can focus on assessing stress-related health risks. Furthermore, if the user is relaxed, the determination unit can also assess the overall health risk in a balanced manner. Furthermore, if the user is in a hurry, the determination unit can quickly assess major health risks. This allows for more appropriate assessment by adjusting the health risk assessment method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the health risk assessment method.

[0073] The determination unit can adjust the level of detail of the determination based on the importance of the data when determining a health risk. The determination unit, for example, performs a detailed health risk determination based on data with high importance. The determination unit can also perform a simplified health risk determination based on data with low importance. The determination unit can also dynamically adjust the level of detail of the determination based on the importance of the data. This enables efficient health risk determination by adjusting the level of detail of the determination based on the importance of the data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the determination.

[0074] The determination unit can apply different determination algorithms depending on the data category when determining health risks. For example, the determination unit applies a medical-specific determination algorithm to medical data. The determination unit can also apply a biological function-specific determination algorithm to biological function measurement information. The determination unit can also apply a lifestyle-specific determination algorithm to lifestyle and dietary data. This enables highly accurate health risk determination by applying different determination algorithms depending on the data category. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the data category to the generation AI and cause the generation AI to apply different determination algorithms.

[0075] The determination unit can improve the accuracy of the health risk determination by referring to the user's past determination results. For example, the determination unit can improve the current determination accuracy by referring to the user's past health risk determination results. The determination unit can also focus on determining specific health risks based on the user's past determination results. The determination unit can also analyze the user's past determination results and optimize the determination algorithm. This can improve the current determination accuracy by referring to the past determination results. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's past determination result data into the generation AI and cause the generation AI to improve the determination accuracy.

[0076] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user emotion. For example, if the user is feeling stressed, the determination unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the determination unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method of the determination result according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0077] When assessing health risks, the assessment unit can determine the priority of assessment based on the time of data submission. For example, the assessment unit prioritizes the most recently submitted data. The assessment unit can also lower the priority of older submitted data. The assessment unit can also dynamically adjust the priority of assessment based on the time of submission. This enables efficient health risk assessment by determining the priority of assessment based on the time of data submission. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input the time of data submission to the generation AI and have the generation AI determine the priority of assessment.

[0078] The determination unit can adjust the order of determination based on the relevance of data when determining health risks. For example, the determination unit prioritizes determination of highly relevant data. The determination unit can also postpone the order of determination of less relevant data. The determination unit can also dynamically adjust the order of determination based on the relevance of data. This enables efficient health risk determination by adjusting the order of determination based on the relevance of data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of determination.

[0079] When determining a health risk, the determination unit can adjust the use of technical terms in the determination result according to the user's level of expertise. For example, the determination unit can explain the determination result in simple terms to a user with little technical knowledge. The determination unit can also explain the determination result using detailed technical terms to a user with extensive technical knowledge. The determination unit can also dynamically adjust the method of explaining the determination result according to the user's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terms in the determination result according to the user's level of expertise. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0080] The providing unit can estimate the user's emotions and adjust the health risk notification method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can select a gentle notification method. Furthermore, if the user is relaxed, the providing unit can select a detailed notification method. Furthermore, if the user is in a hurry, the providing unit can select a concise notification method. This allows for more appropriate information provision by adjusting the health risk notification method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0081] When notifying a health risk, the providing unit can adjust the level of detail of the notification based on the importance of the risk. For example, the providing unit provides a detailed notification for a risk with a high importance. The providing unit can also provide a simplified notification for a risk with a low importance. The providing unit can also dynamically adjust the level of detail of the notification according to the importance of the risk. This enables efficient information provision by adjusting the level of detail of the notification according to the importance of the risk. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the risk to the generating AI and cause the generating AI to adjust the level of detail of the notification.

[0082] When notifying a health risk, the providing unit can apply different notification algorithms depending on the risk category. For example, the providing unit can apply a notification algorithm specialized for heart disease to heart disease risk. The providing unit can also apply a notification algorithm specialized for diabetes to diabetes risk. The providing unit can also apply a notification algorithm specialized for cancer to cancer risk. This makes it possible to provide highly accurate information by applying different notification algorithms depending on the risk category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the risk category into the generating AI and cause the generating AI to apply different notification algorithms.

[0083] When notifying a health risk, the providing unit can improve the accuracy of the notification by referring to the user's past notification results. For example, the providing unit can improve the accuracy of the current notification by referring to the user's past health risk notification results. The providing unit can also focus the notification on specific risks based on the user's past notification results. The providing unit can also analyze the user's past notification results and optimize the notification algorithm. In this way, the accuracy of the current notification can be improved by referring to the past notification results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past notification result data into the generation AI and cause the generation AI to improve the accuracy of the notification.

[0084] The providing unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a short, to-the-point notification. If the user is relaxed, the providing unit can also provide a longer notification with detailed information. If the user is in a hurry, the providing unit can also provide a quick, concise notification. This allows for more appropriate information provision by adjusting the length of the notification according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0085] When notifying a health risk, the providing unit can determine the priority of notifications based on the time of submission of the risk. For example, the providing unit prioritizes notification of recently submitted risk information. The providing unit can also lower the priority of risk information that was submitted earlier. The providing unit can also dynamically adjust the priority of notifications based on the time of submission. This enables efficient information provision by determining the priority of notifications based on the time of submission of the risk. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the risk to the generating AI and have the generating AI determine the priority of notifications.

[0086] The providing unit can adjust the order of notifications based on the relevance of the risks when notifying health risks. For example, the providing unit prioritizes the notification of highly relevant risk information. The providing unit can also postpone the order of notifications for less relevant risk information. The providing unit can also dynamically adjust the order of notifications based on the relevance of the risk information. This enables efficient information provision by adjusting the order of notifications based on the relevance of the risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the risk information to the generating AI and cause the generating AI to adjust the order of notifications.

[0087] When notifying a user of a health risk, the providing unit can adjust the use of technical terminology in the notification according to the user's level of expertise. For example, the providing unit can provide a notification in simple language to a user with little technical expertise. The providing unit can also provide a notification using detailed technical terminology to a user with extensive technical expertise. The providing unit can also dynamically adjust the content of the notification according to the user's level of expertise. This enables more appropriate information to be provided by adjusting the use of technical terminology in the notification according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into a generating AI and cause the generating AI to use technical terminology.

[0088] The linking unit can estimate the user's emotions and select services to link based on the estimated user emotions. For example, if the user is feeling stressed, the linking unit can prioritize linking services that have a relaxing effect. Furthermore, if the user is relaxed, the linking unit can prioritize linking services that are useful for health management. Furthermore, if the user is in a hurry, the linking unit can prioritize linking services that can respond quickly. This allows for more appropriate service provision by selecting services to link based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI select a service.

[0089] When linking services, the linking unit can select the optimal linking method based on the user's past linking history. For example, the linking unit prioritizes linking services that the user has used in the past. The linking unit can also suggest the optimal linking method based on the user's past linking history. The linking unit can also analyze the user's past linking history and optimize the linking method. This makes it possible to provide the optimal linking method by referring to the past linking history. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's past linking history data into the generation AI and cause the generation AI to select the optimal linking method.

[0090] The collaboration unit can customize the collaboration content based on the user's current health condition during service collaboration. The collaboration unit customizes the content of the service to be collaborated based on, for example, the user's current health condition. The collaboration unit can also adjust the priority of the services to be collaborated according to the user's health condition. The collaboration unit can also customize the method of providing the services to be collaborated based on the user's health condition. This enables more appropriate service provision by customizing the collaboration content based on the user's current health condition. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's health condition data into the generation AI and cause the generation AI to customize the collaboration content.

[0091] The collaboration unit can improve the collaboration method by reflecting user feedback during service collaboration. The collaboration unit improves the collaboration method based on, for example, user feedback. The collaboration unit can also propose an optimal collaboration method based on the user's past feedback. The collaboration unit can also improve the collaboration interface by referring to the user's feedback. In this way, the collaboration method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0092] The linking unit can estimate the user's emotions and determine the priority of linking based on the estimated user's emotions. For example, if the user is feeling stressed, the linking unit can prioritize linking services that have a relaxing effect. Furthermore, if the user is relaxed, the linking unit can prioritize linking services that are useful for health management. Furthermore, if the user is in a hurry, the linking unit can prioritize linking services that can respond quickly. This enables more appropriate service provision by determining the priority of linking based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of linking.

[0093] The linking unit can select the optimal linking method when linking services, taking into account the user's geographical location information. For example, when the user is in a specific area, the linking unit prioritizes linking services related to that area. Furthermore, when the user is traveling, the linking unit can also prioritize linking services related to the travel destination. Furthermore, when the user is at home, the linking unit can also prioritize linking services related to the home. In this way, the optimal linking method can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal linking method.

[0094] When linking services, the linking unit can analyze the user's social media activity and suggest linking content. For example, the linking unit can suggest related services based on health information shared by the user on social media. The linking unit can also analyze the content posted by the user on social media and suggest related services. The linking unit can also suggest related services by referring to the activities of the user's friends on social media. In this way, related services can be efficiently suggested by analyzing social media activity. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into the generation AI and have the generation AI suggest linking content.

[0095] The collaboration unit can customize the collaboration method by reflecting the user's past feedback when collaborating with services. The collaboration unit customizes the collaboration method based on, for example, the user's past feedback. The collaboration unit can also propose an optimal collaboration method based on the user's past feedback. The collaboration unit can also improve the collaboration interface by referring to the user's past feedback. In this way, the collaboration method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collaboration method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, determination unit, provision unit, and linkage unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from the user. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generative AI to determine current and future health risks. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the user with the health risks determined by the determination unit. The linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with an online health consultation and medical service based on the information provided by the provision unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, determination unit, provision unit, and linkage unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of medical data, medical history, biometric information, and lifestyle or dietary data from the user. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generative AI to determine current and future health risks. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with the health risks determined by the determination unit. The linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with an online health consultation and medical service based on the information provided by the provision unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. Emotion estimation is performed using, for example, the camera 42 and microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, determination unit, provision unit, and linkage unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314 and receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from the user. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generative AI to determine current and future health risks. The provision unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and provides the user with the health risks determined by the determination unit. The linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with an online health consultation and medical service based on the information provided by the provision unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. The emotion estimation is performed using, for example, the camera 42 and microphone 238 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, determination unit, provision unit, and linkage unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from the user. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generative AI to determine current and future health risks. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the user with the health risks determined by the determination unit. The linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with an online health consultation and medical service based on the information provided by the provision unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] When the user enters health data, the reception unit can analyze the user's past data input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user tends to input data during a specific time period, the reception unit can send a notification prompting input during that time period. Furthermore, the reception unit can select the optimal input method based on the type of data the user has input in the past. In this way, by analyzing the user's past data input history, the reception unit can offer the optimal input method to the user.

[0098] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the timing of data input can be delayed to encourage the user to input in a relaxed state. Also, if the user is relaxed, the timing of data input can be accelerated to allow efficient input. Furthermore, if the user is in a hurry, the timing of data input can be adjusted to complete input in a short time. Thus, by adjusting the timing of data input according to the user's emotions, more appropriate data input is possible.

[0099] The reception unit can perform filtering based on the user's current health condition and areas of interest when inputting data. For example, data items that require input can be filtered based on the user's current health condition. Data items that require input can also be preferentially displayed based on the user's areas of interest. Furthermore, data items that do not require input can also be hidden based on the user's health condition and areas of interest. This allows for efficient data input by filtering data input based on the user's health condition and areas of interest.

[0100] When inputting data, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the reception unit can input data using voice recognition technology. If the user desires text input, the reception unit can also provide keyboard input. Furthermore, if the user desires image input, the reception unit can also input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method.

[0101] The reception unit can estimate the user's emotions and determine the priority of input data based on the estimated user's emotions. For example, if the user is feeling stressed, important data items can be input with priority. Also, if the user is relaxed, detailed data items can be input with priority. Furthermore, if the user is in a hurry, the minimum number of data items can be input with priority. In this way, by determining the priority of input data according to the user's emotions, important data can be input with priority.

[0102] The determination unit can adjust the level of detail of the determination based on the importance of the data when determining a health risk. For example, a detailed health risk determination is made based on data with high importance. A simplified health risk determination can also be made based on data with low importance. Furthermore, the level of detail of the determination can be dynamically adjusted according to the importance of the data. This allows for efficient health risk determination by adjusting the level of detail of the determination according to the importance of the data.

[0103] When determining health risks, the determination unit can apply different determination algorithms depending on the data category. For example, a medical-specific determination algorithm can be applied to medical data. A biological function-specific determination algorithm can also be applied to biological function measurement information. Furthermore, a lifestyle-specific determination algorithm can also be applied to lifestyle and dietary data. This allows for highly accurate health risk determination by applying different determination algorithms depending on the data category.

[0104] The assessment unit can estimate the user's emotions and adjust the health risk assessment method based on the estimated user emotions. For example, if the user is feeling stressed, the assessment can focus on stress-related health risks. If the user is relaxed, the overall health risks can be assessed in a balanced manner. Furthermore, if the user is in a hurry, the major health risks can be assessed quickly. This allows for more appropriate assessment by adjusting the health risk assessment method according to the user's emotions.

[0105] The providing unit can estimate the user's emotions and adjust the health risk notification method based on the estimated user's emotions. For example, if the user is feeling stressed, a gentle notification method can be selected. Alternatively, if the user is relaxed, a detailed notification method can be selected. Furthermore, if the user is in a hurry, a concise notification method can be selected. In this way, by adjusting the health risk notification method according to the user's emotions, more appropriate information can be provided.

[0106] The linking unit can estimate the user's emotions and select services to link with based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize linking with services that have a relaxing effect. Also, if the user is relaxed, it can prioritize linking with services that are useful for health management. Furthermore, if the user is in a hurry, it can prioritize linking with services that can respond quickly. In this way, by selecting services to link with based on the user's emotions, it becomes possible to provide more appropriate services.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit accepts input of medical data, medical history, biometric information, lifestyle or dietary data from the user. For example, this may include the results of a health checkup, medical examination, medical examination results, past medical history, surgical history, allergy information, heart rate, blood pressure, body temperature, exercise habits, smoking habits, drinking habits, dietary content, calorie intake, and nutrient information. Step 2: The judgment unit uses the generation AI to analyze the information received by the reception unit and determine current and future health risks. For example, it analyzes health checkup results to determine the risk of heart disease, analyzes smartwatch data to determine future health risks due to lack of exercise, and analyzes dietary content to point out nutritional imbalances. Step 3: The provider provides the user with the health risks determined by the determiner. For example, the provider displays the health risks determined by the generator AI, such as the risk of heart disease, health risks due to lack of exercise, and nutritional imbalance, via a web or mobile application to notify the user. Step 4: The linking unit links with online health consultation and medical services based on the information provided by the provider. For example, when a user uses online health consultation, the AI ​​generation unit provides the analyzed health risk information, and a doctor uses this information to make a diagnosis and provide advice.

[0109] 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.

[0110] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of medical data, medical history, biological function measurement information, and lifestyle or dietary data from a user; a determination unit that analyzes the information received by the reception unit and determines current health risks and future health risks; a providing unit that provides a user with the health risk determined by the determining unit; a linking unit that links with online health consultation and medical services based on the information provided by the providing unit; A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past data entry history and select the appropriate entry method 2. The system of claim 1.

4. The reception unit Filtering data entry based on the user's current health status and areas of interest 2. The system of claim 1.

5. The reception unit When entering data, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input data based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Prioritize relevant data based on the user's geographic location during data entry 2. The system of claim 1.

8. The reception unit Analyze users' social media activity and enter relevant data when entering data 2. The system of claim 1.

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